WO2024091858A1 - Action suivante déterministe atomique - Google Patents

Action suivante déterministe atomique Download PDF

Info

Publication number
WO2024091858A1
WO2024091858A1 PCT/US2023/077478 US2023077478W WO2024091858A1 WO 2024091858 A1 WO2024091858 A1 WO 2024091858A1 US 2023077478 W US2023077478 W US 2023077478W WO 2024091858 A1 WO2024091858 A1 WO 2024091858A1
Authority
WO
WIPO (PCT)
Prior art keywords
atomic
deterministic
next action
action task
task block
Prior art date
Application number
PCT/US2023/077478
Other languages
English (en)
Inventor
Sharath Venkatarao
Daniel Brian STEEVES
Sandeep Kulkarni
III James M. ADKINS
Original Assignee
Dish Wireless L.L.C.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US18/341,635 external-priority patent/US20240147259A1/en
Priority claimed from US18/341,632 external-priority patent/US20240143384A1/en
Application filed by Dish Wireless L.L.C. filed Critical Dish Wireless L.L.C.
Publication of WO2024091858A1 publication Critical patent/WO2024091858A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0793—Remedial or corrective actions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3604—Software analysis for verifying properties of programs
    • G06F11/3612—Software analysis for verifying properties of programs by runtime analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/48—Indexing scheme relating to G06F9/48
    • G06F2209/481—Exception handling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/48—Indexing scheme relating to G06F9/48
    • G06F2209/486—Scheduler internals

Definitions

  • 5G mobile networks have the promise to provide higher throughput, lower latency, and higher availability compared with previous global wireless standards.
  • CUPS control and user plane separation
  • MEC multi-access edge computing
  • 5G networks may leverage the use of cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM) to increase channel utilization and reduce interference, the use of multiple-input multiple-output (MIMO) antennas to increase spectral efficiency, and the use of millimeter wave spectrum (mmWave) operation to increase throughput and reduce latency in data transmission.
  • 5G wireless user equipment UE may communicate over both a lower frequency sub-6 GHz band between 410 MHz and 7125 MHz and a higher frequency mmWave band between 24.25 GHz and 52.6 GHz.
  • lower frequencies may provide a lower maximum bandwidth and lower data rates than higher frequencies, lower frequencies may provide higher spectral efficiency and greater range.
  • the mmWave spectrum may provide higher data rates, the millimeter waves may not penetrate through objects, such as walls and glass, and may have a more limited range.
  • ADNA atomic deterministic next action
  • An ADNA may invoke one or more other ADNAs without requiring a request-response pattern when communicating with the one or more other ADNAs.
  • an ADNA Manager may be utilized to orchestrate the execution of a workflow process that includes sub-processes and/or tasks without a fixed orchestration.
  • the total number of sub-processes and/or tasks for the workflow process may be unknown until runtime of the workflow process.
  • the ADNA Manager may identify a first ADNA task block out of a pool of ADNA task blocks managed by the ADNA Manager, identify a set of input parameters for the first ADNA task block based on the workflow process, detect that a first input parameter of the set of input parameters does not satisfy a qualification rule for the first ADNA task block, identify an exception ADNA task block out of the pool of ADNA task blocks in response to detection that the first input parameter does not satisfy the qualification rule, store breadcrumb information for the first ADNA task block within a persistence layer prior to the exception ADNA task block being invoked, and invoke the exception ADNA task block.
  • the breadcrumb information for the first ADNA task block may include a timestamp for when the first ADNA task block invoked the exception ADNA task block, an identification of the first ADNA task block, and an identification of the exception ADNA task block.
  • the system includes one or more processors and a memory that stores computer instructions that when executed by the one or more processors, cause the system to: identify a first atomic deterministic next action task block out of a pool of atomic deterministic next action task blocks associated with one or more workflow rules; detect that a first input parameter does not satisfy a qualification rule for the first atomic deterministic next action task block; identify and invoke an exception atomic deterministic next action task block, in response to detection that the first input parameter does not satisfy the qualification rule; detect that an exception atomic deterministic next action task block has been invoked more than a threshold number of times; repair the first atomic deterministic next action task block in response to detection that the exception atomic deterministic next action task block has been invoked more than the threshold number of times; add a new atomic deterministic next action task block to the pool of atomic deterministic next action task blocks; add a new workflow rule that acquires an updated input parameter
  • the new atomic deterministic next action task block is associated with one or more of a new hardware device added to the system and a new virtual device added to the system.
  • the new atomic deterministic next action task block corresponds with a newly instantiated virtualized network.
  • the threshold number of times is more than ten times.
  • the repaired first atomic deterministic next action task block includes a new workflow rule to acquire an updated input parameter.
  • the one or more processors are configured to store breadcrumb information for the first atomic deterministic next action task block within a persistence layer prior to the repaired first atomic deterministic next action task block being invoked.
  • the breadcrumb information for the repaired first atomic deterministic next action task block includes a timestamp for when the repaired first atomic deterministic next action task block invoked the new atomic deterministic next action task block.
  • the breadcrumb information for the repaired first atomic deterministic next action task block includes an identification of the repaired first atomic deterministic next action task block and an identification of the new atomic deterministic next action task block.
  • the one or more processors are configured to detect that each input parameter satisfies one or more qualification rules and executes one or more workflow rules for the new atomic deterministic next action task block.
  • an updated first atomic deterministic next action task block method includes: identifying, using a processor, a first atomic deterministic next action task block out of a pool of atomic deterministic next action task blocks associated with one or more workflow rules; detecting, using a processor, that a first input parameter does not satisfy a qualification rule for the first atomic deterministic next action task block; invoking, using a processor, an exception atomic deterministic next action task block, in response to detection that the first input parameter does not satisfy the qualification rule; detecting, using a processor, that an exception atomic deterministic next action task block has been invoked more than a threshold number of times; repairing, using a processor, the first atomic deterministic next action task block in response to detection that the exception atomic deterministic next action task block has been invoked more than the threshold number of times; adding, using a processor, a new atomic deterministic next action task block to the pool of atomic deterministic next action task blocks
  • the new atomic deterministic next action task block is associated with one or more of a new hardware device and a new virtual device.
  • the new atomic deterministic next action task block corresponds with a newly instantiated virtualized network.
  • the threshold number of times is more than ten times.
  • the repaired first atomic deterministic next action task block includes a new workflow rule to acquire an updated input parameter.
  • the repaired first atomic deterministic next action task block method further comprises: storing breadcrumb information for the first atomic deterministic next action task block within a persistence layer prior to the repaired first atomic deterministic next action task block being invoked.
  • the breadcrumb information for the repaired first atomic deterministic next action task block includes a timestamp for when the repaired first atomic deterministic next action task block invoked the new atomic deterministic next action task block.
  • the breadcrumb information for the repaired first atomic deterministic next action task block includes an identification of the updated first atomic deterministic next action task block and an identification of the new atomic deterministic next action task block.
  • the repaired first atomic deterministic next action task block method further comprises: detecting that each input parameter satisfies one or more qualification rules and executes one or more workflow rules for the new atomic deterministic next action task block.
  • a system includes one or more processors configured to: detect that an exception atomic deterministic next action task block has been invoked more than a threshold number of times; repair the first atomic deterministic next action task block in response to detection that the exception atomic deterministic next action task block has been invoked more than the threshold number of times; add a new atomic deterministic next action task block to the pool of atomic deterministic next action task blocks; add a new workflow rule that acquires an updated input parameter to the one or more workflow rules for the repaired first atomic deterministic next action task block based on a number of exception atomic deterministic next action task blocks invoked by the repaired atomic deterministic next action task block; and invoke the new atomic deterministic next action task block using the repaired first atomic deterministic next action task block.
  • the new atomic deterministic next action task block is associated with one or more of a new hardware device added to the system and a new virtual device added to the system.
  • the new atomic deterministic next action task block corresponds with a newly instantiated virtualized network.
  • the threshold number of times is more than ten times.
  • the repaired first atomic deterministic next action task block includes a new workflow rule to acquire an updated input parameter.
  • the one or more processors are configured to store breadcrumb information for the first atomic deterministic next action task block within a persistence layer prior to the repaired first atomic deterministic next action task block being invoked.
  • the breadcrumb information for the repaired first atomic deterministic next action task block includes a timestamp for when the repaired first atomic deterministic next action task block invoked the new atomic deterministic next action task block.
  • the breadcrumb information for the repaired first atomic deterministic next action task block includes an identification of the repaired first atomic deterministic next action task block and an identification of the new atomic deterministic next action task block.
  • the one or more processors are configured to detect that each input parameter satisfies one or more qualification rules and executes one or more workflow rules for the new atomic deterministic next action task block.
  • the technical improvements of the systems and methods disclosed herein include improved system performance, fault tolerance, and load balancing. Furthermore, the number of request-response transactions between tasks managed by the ADNA Manager may be eliminated or reduced, thereby reducing system power and energy consumption.
  • Figure 1 A depicts an embodiment of a 5G network including a radio access network (RAN) and a core network.
  • RAN radio access network
  • Figures IB and 1C depict embodiments of a radio access network and a core network for providing a communications channel (or channel) between user equipment and a data network.
  • Figures 2A-2C depict embodiments of a radio access network.
  • Figure 2D depicts an embodiment of a core network.
  • Figure 2E depicts an embodiment of a containerized environment that includes a container engine running on top of a host operating system.
  • Figure 3 A depicts one embodiment of a microservices orchestration for performing a process.
  • Figure 3B depicts one embodiment of a process performed using a plurality of atomic deterministic next action (ADNA) task blocks.
  • Figure 3C depicts one embodiment of an ADNA.
  • Figure 3D depicts one embodiment of the process performed in Figure 3B in which an exception ADNA was invoked.
  • Figure 3E depicts one embodiment of the process performed in Figure 3B in which an ADNA has been updated to reference a new ADNA.
  • Figure 3F depicts one embodiment of an updated ADNA.
  • Figure 3G depicts one embodiment of an exception ADNA.
  • Figure 4 is a logic diagram showing number sequencing data flow with respect to atomic deterministic next action (ADNA) task block manager.
  • Figure 5 shows a system diagram that describes an example implementation of a computing system(s) for implementing embodiments described herein.
  • ADNA Manager may orchestrate the execution of processes that include sub-processes and/or tasks without a fixed orchestration such that the number of sub-processes and/or tasks is not determined until runtime.
  • a process e.g., a workflow process
  • a process may comprise a set of sub-processes and/or tasks that need to be performed to complete the process.
  • a task may comprise an atomic activity, while a subprocess may comprise a non-atomic activity.
  • a task may comprise a lowest-level process that cannot be broken down to a finer level of detail.
  • Decentralized atomic decision making may be performed using atomic deterministic next action (ADNA) task blocks that execute one or more workflow rules and then call or invoke one or more ADNAs within a pool of ADNAs managed by the ADNA Manager. Over time, ADNAs may be added to and removed from the pool of ADNAs by the ADNA Manager. Each ADNA in the pool of ADNAs may reference or point to one or more other ADNAs within the pool of ADNAs as next action ADNAs. Each ADNA may comprise an API or an interface specification for interfacing with the ADNA, one or more workflow rules to be performed by the ADNA, and a mapping of one or more next actions to one or more other ADNAs within the pool of ADNAs. Each ADNA in the pool of ADNAs may store state information, transaction information, and data processing information within a persistence layer or within a persistent storage layer. The persistent storage layer may comprise nonvolatile data storage.
  • Technical benefits of utilizing an ADNA Manager with decentralized atomic decision making include improved system scalability and a reduction in the number of transactions between operations and/or tasks executed by the ADNA Manager. Moreover, technical benefits of using exception ADNAs to remediate data or perform exception handling for input parameters that do not satisfy qualification rules include improved system performance and reduced system downtime.
  • the ADNA Manager may identify a first ADNA task block out of a pool of ADNA task blocks managed by the ADNA Manager, determine a set of input parameters for the first ADNA task block, detect that a first input parameter of the set of input parameters does not satisfy a qualification rule for the first ADNA task block, identify an exception ADNA task block out of the pool of ADNA task blocks in response to detection that the first input parameter does not satisfy the qualification rule, store breadcrumb information for the first ADNA task block within a persistence layer prior to the exception ADNA task block being invoked, and invoke the exception ADNA task block.
  • the exception ADNA task block may acquire or determine an updated input parameter for the first input parameter and invoke the first ADNA task block from the exception ADNA task block with the updated input parameter.
  • the breadcrumb information for the first ADNA task block may include a timestamp for when the first ADNA task block invoked the exception ADNA task block, an identification of the first ADNA task block (e.g., an alphanumeric string that uniquely identifies the first ADNA task block), and an identification of the exception ADNA task block.
  • microservices may refer to a way of designing a software application as a suite of independently deployable services that typically each run in their own process and communicate through application programming interfaces (APIs), such as an HTTP resource API.
  • APIs application programming interfaces
  • a microservice may require a request-response or request-reply pattern when communicating with other microservices.
  • a requester or initiator of a communication sends a request message to a microservice and then waits for a corresponding response message before timing out or proceeding.
  • an ADNA task block does not use or require a request-response pattern when communicating with other ADNAs.
  • an ADNA task block acquires a set of input parameters, executes one or more workflow rules, and then invokes another ADNA based on an outcome of the execution of the one or more workflow rules.
  • the one or more workflow rules may comprise flow logic (or logic) that implement one or more workflows that correspond with an enterprise process or a portion thereof.
  • the flow logic may correspond with program code (e.g., a script or other form of machine executable instructions) that is stored in a persistence layer or using a non-volatile memory.
  • An API may comprise a set of rules and protocols that define how applications connect to and communicate with each other.
  • a REST API may comprise an API that conforms to the design principles of the representational state transfer (REST) architectural style.
  • REST APIs may be referred to as RESTful APIs.
  • REST APIs provide a flexible, lightweight way to integrate applications, and have emerged as the most common method for connecting components in microservices architectures.
  • REST APIs communicate via HTTP requests to perform standard database functions like creating, reading, updating, and deleting records (also known as CRUD) within a resource.
  • a creation operation may comprise a POST operation
  • a reading operation may comprise a GET operation
  • an updating operation may comprise a PUT operation
  • a delete operation may comprise a DELETE operation.
  • a REST API may use a GET request to retrieve a record, a POST request to create a record, a PUT request to update a record, and a DELETE request to delete a record.
  • a client request is made via a RESTful API, it transfers a representation of the state of the resource to the requester or endpoint.
  • the state of a resource at any particular instant, or timestamp is known as the resource representation.
  • a dynamically scalable ADNA Manager with decentralized atomic decision making may execute processes related to the operation of a 5G network.
  • the ADNA Manager may orchestrate the execution of processes related to the creating and maintenance of network slices using a pool of ADNAs.
  • an ADNA Manager may manage a pool of ADNAs (e.g., twenty thousand ADNAs) that are responsible for performing core network functions.
  • FIG. 1 A depicts an embodiment of a 5G network 102 including a radio access network (RAN) 120 and a core network 130.
  • the radio access network 120 may comprise a new-generation radio access network (NG-RAN) that uses the 5G new radio interface (NR).
  • the 5G network 102 connects user equipment (UE) 108 to the data network (DN) 180 using the radio access network 120 and the core network 130.
  • the data network 180 may comprise the Internet, a local area network (LAN), a wide area network (WAN), a private data network, a wireless network, a wired network, or a combination of networks.
  • the UE 108 may comprise an electronic device with wireless connectivity or cellular communication capability, such as a mobile phone or handheld computing device.
  • the UE 108 may comprise a 5G smartphone or a 5G cellular device that connects to the radio access network 120 via a wireless connection.
  • the UE 108 may comprise one of a number of UEs not depicted that are in communication with the radio access network 120.
  • the UEs may include mobile and non- mobile computing devices.
  • the UEs may include laptop computers, desktop computers, Intemet-of-Things (loT) devices, and/or any other electronic computing device that includes a wireless communications interface to access the radio access network 120.
  • LoT Intemet-of-Things
  • the radio access network 120 includes a remote radio unit (RRU) 202 for wirelessly communicating with UE 108.
  • the remote radio unit (RRU) 202 may comprise a radio unit (RU) and may include one or more radio transceivers for wirelessly communicating with UE 108.
  • the remote radio unit (RRU) 202 may include circuitry for converting signals sent to and from an antenna of a base station into digital signals for transmission over packet networks.
  • the radio access network 120 may correspond with a 5G radio base station that connects user equipment to the core network 130.
  • the 5G radio base station may be referred to as a generation Node B, a “gNodeB,” or a “gNB.”
  • a base station may refer to a network element that is responsible for the transmission and reception of radio signals in one or more cells to or from user equipment, such as UE 108.
  • the core network 130 may utilize a cloud-native service-based architecture (SB A) in which different core network functions (e.g., authentication, security, session management, and core access and mobility functions) are virtualized and implemented as loosely coupled independent services that communicate with each other, for example, using HTTP protocols and APIs.
  • SB A cloud-native service-based architecture
  • core network functions e.g., authentication, security, session management, and core access and mobility functions
  • CP control plane
  • CP control plane
  • a microservices-based architecture in which software is composed of small independent services that communicate over well-defined APIs may be used for implementing some of the core network functions.
  • control plane (CP) network functions for performing session management may be implemented as containerized applications or microservices.
  • a container-based implementation may offer improved scalability and availability over other approaches.
  • Network functions that have been implemented using microservices may store their state information using the unstructured data storage function (UDSF) that supports data storage for stateless network functions across the service-based architecture (SB A).
  • UDSF unstructured data storage function
  • the primary core network functions may comprise the access and mobility management function (AMF), the session management function (SMF), and the user plane function (UPF).
  • the UPF e.g., UPF 132
  • the UPF may perform packet processing including routing and forwarding, quality of service (QoS) handling, and packet data unit (PDU) session management.
  • the UPF may serve as an ingress and egress point for user plane traffic and provide anchored mobility support for user equipment.
  • the UPF 132 may provide an anchor point between the UE 108 and the data network 180 as the UE 108 moves between coverage areas.
  • the AMF may act as a single-entry point for a UE connection and perform mobility management, registration management, and connection management between a data network and UE.
  • the SMF may perform session management, user plane selection, and IP address allocation.
  • Other core network functions may include a network repository function (NRF) for maintaining a list of available network functions and providing network function service registration and discovery, a policy control function (PCF) for enforcing policy rules for control plane functions, an authentication server function (AUSF) for authenticating user equipment and handling authentication related functionality, a network slice selection function (NSSF) for selecting network slice instances, and an application function (AF) for providing application services.
  • NRF network repository function
  • PCF policy control function
  • AUSF authentication server function
  • NSSF network slice selection function
  • AF application function
  • Application-level session information may be exchanged between the AF and PCF (e.g., bandwidth requirements for QoS).
  • the PCF may dynamically decide if the user equipment should grant the requested access based on a location of the user equipment.
  • a network slice may comprise an independent end-to-end logical communications network that includes a set of logically separated virtual network functions.
  • Network slicing may allow different logical networks or network slices to be implemented using the same compute and storage infrastructure. Therefore, network slicing may allow heterogeneous services to coexist within the same network architecture via allocation of network computing, storage, and communication resources among active services.
  • the network slices may be dynamically created and adjusted over time based on network requirements. For example, some networks may require ultra-low-latency or ultra-reliable services.
  • components of the radio access network 120 may need to be deployed at a cell site or in a local data center (LDC) that is in close proximity to a cell site such that the latency requirements are satisfied (e.g., such that the one-way latency from the cell site to the DU component or CU component is less than 1.2ms).
  • LDC local data center
  • the distributed unit (DU) and the centralized unit (CU) of the radio access network 120 may be co-located with the remote radio unit (RRU) 202.
  • the distributed unit (DU) and the remote radio unit (RRU) 202 may be co-located at a cell site and the centralized unit (CU) may be located within a local data center (LDC).
  • LDC local data center
  • the 5G network 102 may provide one or more network slices, wherein each network slice may include a set of network functions that is selected to provide specific telecommunications services.
  • each network slice may comprise a configuration of network functions, network applications, and underlying cloud-based compute and storage infrastructure.
  • a network slice may correspond with a logical instantiation of a 5G network, such as an instantiation of the 5G network 102.
  • the 5G network 102 may support customized policy configuration and enforcement between network slices per service level agreements (SLAs) within the radio access network (RAN) 120.
  • SLAs service level agreements
  • User equipment such as UE 108, may connect to multiple network slices at the same time (e.g., eight different network slices).
  • a PDU session such as PDU session 104, may belong to only one network slice instance.
  • a cloud-based compute and storage infrastructure may comprise a networked computing environment that provides a cloud computing environment.
  • Cloud computing may refer to Internet-based computing, wherein shared resources, software, and/or information may be provided to one or more computing devices on-demand via the Internet (or other network).
  • the term “cloud” may be used as a metaphor for the Internet, based on the cloud drawings used in computer networking diagrams to depict the Internet as an abstraction of the underlying infrastructure it represents.
  • the core network 130 may include a plurality of network elements that are configured to offer various data and telecommunications services to subscribers or end users of user equipment, such as UE 108.
  • network elements include network computers, network processors, networking hardware, networking equipment, routers, switches, hubs, bridges, radio network controllers, gateways, servers, virtualized network functions, and network functions virtualization infrastructure.
  • a network element may comprise a real or virtualized component that provides wired or wireless communication network services.
  • Virtualization allows virtual hardware to be created and decoupled from the underlying physical hardware.
  • a virtualized component is a virtual router (or a vRouter).
  • Another example of a virtualized component is a virtual machine.
  • a virtual machine may comprise a software implementation of a physical machine.
  • the virtual machine may include one or more virtual hardware devices, such as a virtual processor, a virtual memory, a virtual disk, or a virtual network interface card.
  • the virtual machine may load and execute an operating system and applications from the virtual memory.
  • the operating system and applications used by the virtual machine may be stored using the virtual disk.
  • the virtual machine may be stored as a set of files including a virtual disk file for storing the contents of a virtual disk and a virtual machine configuration file for storing configuration settings for the virtual machine.
  • the configuration settings may include the number of virtual processors (e.g., four virtual CPUs), the size of a virtual memory, and the size of a virtual disk (e.g., a 64GB virtual disk) for the virtual machine.
  • a virtualized component is a software container or an application container that encapsulates an application’s environment.
  • the 5G network 102 may implement various network functions, such as the core network functions and radio access network functions, using a cloud-based compute and storage infrastructure.
  • a network function may be implemented as a software instance running on hardware or as a virtualized network function.
  • Virtual network functions VNFs may comprise implementations of network functions as software processes or applications.
  • a virtual network function VNF may be implemented as a software process or application that is run using virtual machines (VMs) or application containers within the cloudbased compute and storage infrastructure.
  • Application containers allow applications to be bundled with their own libraries and configuration files, and then executed in isolation on a single operating system (OS) kernel.
  • Application containerization may refer to an OS-level virtualization method that allows isolated applications to be run on a single host and access the same OS kernel.
  • Containers may run on bare-metal systems, cloud instances, and virtual machines.
  • Network functions virtualization may be used to virtualize network functions, for example, via virtual machines, containers, and/or virtual hardware that runs processor readable code or executable instructions stored in one or more computer-readable storage mediums (e.g., one or more data storage devices).
  • the core network 130 includes a user plane function (UPF) 132 for transporting IP data traffic (e.g., user plane traffic) between the UE 108 and the data network 180 and for handling packet data unit (PDU) sessions with the data network 180.
  • the UPF 132 may comprise an anchor point between the UE 108 and the data network 180.
  • the UPF 132 may be implemented as a software process or application running within a virtualized infrastructure or a cloud-based compute and storage infrastructure.
  • the 5G network 102 may connect the UE 108 to the data network 180 using a packet data unit (PDU) session 104, which may comprise part of an overlay network.
  • PDU packet data unit
  • the PDU session 104 may utilize one or more quality of service (QoS) flows, such as QoS flows 105 and 106, to exchange traffic (e.g., data and voice traffic) between the UE 108 and the data network 180.
  • QoS quality of service
  • the one or more QoS flows may comprise the finest granularity of QoS differentiation within the PDU session 104.
  • the PDU session 104 may belong to a network slice instance through the 5G network 102.
  • an AMF that supports the network slice instance may be selected and a PDU session via the network slice instance may be established.
  • the PDU session 104 may be of type IPv4 or IPv6 for transporting IP packets.
  • the radio access network 120 may be configured to establish and release parts of the PDU session 104 that cross the radio interface.
  • the radio access network 120 may include a set of one or more remote radio units (RRUs) that includes radio transceivers (or combinations of radio transmitters and receivers) for wirelessly communicating with UEs.
  • the set of RRUs may correspond with a network of cells (or coverage areas) that provide continuous or nearly continuous overlapping service to UEs, such as UE 108, over a geographic area. Some cells may correspond with stationary coverage areas and other cells may correspond with coverage areas that change over time (e.g., due to movement of a mobile RRU).
  • the UE 108 may be capable of transmitting signals to and receiving signals from one or more RRUs within the network of cells over time.
  • One or more cells may correspond with a cell site.
  • the cells within the network of cells may be configured to facilitate communication between UE 108 and other UEs and/or between UE 108 and a data network, such as data network 180.
  • the cells may include macrocells (e.g., capable of reaching 18 miles) and small cells, such as microcells (e.g., capable of reaching 1.2 miles), picocells (e.g., capable of reaching 0.12 miles), and femtocells (e.g., capable of reaching 32 feet). Small cells may communicate through macrocells.
  • Macrocells may transit and receive radio signals using multiple-input multipleoutput (MIMO) antennas that may be connected to a cell tower, an antenna mast, or a raised structure.
  • MIMO multiple-input multipleoutput
  • the UPF 132 may be responsible for routing and forwarding user plane packets between the radio access network 120 and the data network 180.
  • Uplink packets arriving from the radio access network 120 may use a general packet radio service (GPRS) tunneling protocol (or GTP tunnel) to reach the UPF 132.
  • GPRS general packet radio service
  • the GPRS tunneling protocol for the user plane may support multiplexing of traffic from different PDU sessions by tunneling user data over the interface between the radio access network 120 and the UPF 132.
  • the UPF 132 may remove the packet headers belonging to the GTP tunnel before forwarding the user plane packets towards the data network 180. As the UPF 132 may provide connectivity towards other data networks in addition to the data network 180, the UPF 132 must ensure that the user plane packets are forwarded towards the correct data network.
  • Each GTP tunnel may belong to a specific PDU session, such as PDU session 104.
  • Each PDU session may be set up towards a specific data network name (DNN) that uniquely identifies the data network to which the user plane packets should be forwarded.
  • DNN data network name
  • the UPF 132 may keep a record of the mapping between the GTP tunnel, the PDU session, and the DNN for the data network to which the user plane packets are directed.
  • a QoS flow may correspond with a stream of data packets that have equal quality of service (QoS).
  • QoS quality of service
  • a PDU session may have multiple QoS flows, such as the QoS flows 105 and 106 that belong to PDU session 104.
  • the UPF 132 may use a set of service data flow (SDF) templates to map each downlink packet onto a specific QoS flow.
  • SDF service data flow
  • the UPF 132 may receive the set of SDF templates from a session management function (SMF), such as the SMF 133 depicted in Figure IB, during setup of the PDU session 104.
  • SMF session management function
  • the SMF may generate the set of SDF templates using information provided from a policy control function (PCF), such as the PCF 135 depicted in Figure 1C.
  • PCF policy control function
  • the UPF 132 may track various statistics regarding the volume of data transferred by each PDU session, such as PDU session 104, and provide the information to an SMF.
  • Figure IB depicts an embodiment of a radio access network 120 and a core network 130 for providing a communications channel (or channel) between user equipment and data network 180.
  • the communications channel may comprise a pathway through which data is communicated between the UE 108 and the data network 180.
  • the user equipment in communication with the radio access network 120 includes UE 108, mobile phone 110, and mobile computing device 112.
  • the user equipment may include a plurality of electronic devices, including mobile computing device and non-mobile computing device.
  • the core network 130 includes network functions such as an access and mobility management function (AMF) 134, a session management function (SMF) 133, and a user plane function (UPF) 132.
  • the AMF may interface with user equipment and act as a single-entry point for a UE connection.
  • the AMF may interface with the SMF to track user sessions.
  • the AMF may interface with a network slice selection function (NSSF) not depicted to select network slice instances for user equipment, such as UE 108.
  • NSF network slice selection function
  • the AMF may be responsible for coordinating the handoff between the coverage areas whether the coverage areas are associated with the same radio access network or different radio access networks.
  • the UPF 132 may transfer downlink data received from the data network 180 to user equipment, such as UE 108, via the radio access network 120 and/or transfer uplink data received from user equipment to the data network 180 via the radio access network 180.
  • An uplink may comprise a radio link though which user equipment transmits data and/or control signals to the radio access network 120.
  • a downlink may comprise a radio link through which the radio access network 120 transmits data and/or control signals to the user equipment.
  • the radio access network 120 may be logically divided into a remote radio unit (RRU) 202, a distributed unit (DU) 204, and a centralized unit (CU) that is partitioned into a CU user plane portion CU-UP 216 and a CU control plane portion CU-CP 214.
  • the CU-UP 216 may correspond with the centralized unit for the user plane and the CU-CP 214 may correspond with the centralized unit for the control plane.
  • the CU-CP 214 may perform functions related to a control plane, such as connection setup, mobility, and security.
  • the CU-UP 216 may perform functions related to a user plane, such as user data transmission and reception functions. Additional details of radio access networks are described in reference to Figure 2A.
  • Decoupling control signaling in the control plane from user plane traffic in the user plane may allow the UPF 132 to be positioned in close proximity to the edge of a network compared with the AMF 134. As a closer geographic or topographic proximity may reduce the electrical distance, this means that the electrical distance from the UPF 132 to the UE 108 may be less than the electrical distance of the AMF 134 to the UE 108.
  • the radio access network 120 may be connected to the AMF 134, which may allocate temporary unique identifiers, determine tracking areas, and select appropriate policy control functions (PCFs) for user equipment, via an N2 interface.
  • PCFs policy control functions
  • An N3 interface may be used for transferring user data (e.g., user plane traffic) from the radio access network 120 to the user plane function UPF 132 and may be used for providing low-latency services using edge computing resources.
  • the electrical distance from the UPF 132 (e.g., located at the edge of a network) to user equipment, such as UE 108, may impact the latency and performance services provided to the user equipment.
  • the UE 108 may be connected to the SMF 133 via an N1 interface not depicted, which may transfer UE information directly to the AMF 134.
  • the UPF 132 may be connected to the data network 180 via an N6 interface.
  • the N6 interface may be used for providing connectivity between the UPF 132 and other external or internal data networks (e.g., to the Internet).
  • the radio access network 120 may be connected to the SMF 133, which may manage UE context and network handovers between base stations, via the N2 interface.
  • the N2 interface may be used for transferring control plane signaling
  • the RRU 202 may perform physical layer functions, such as employing orthogonal frequency-division multiplexing (OFDM) for downlink data transmission.
  • the DU 204 may be located at a cell site (or a cellular base station) and may provide realtime support for lower layers of the protocol stack, such as the radio link control (RLC) layer and the medium access control (MAC) layer.
  • the CU may provide support for higher layers of the protocol stack, such as the service data adaptation protocol (SDAP) layer, the packet data convergence control (PDCP) layer, and the radio resource control (RRC) layer.
  • SDAP service data adaptation protocol
  • PDCP packet data convergence control
  • RRC radio resource control
  • the SDAP layer may comprise the highest L2 sublayer in the 5GNR protocol stack.
  • a radio access network may correspond with a single CU that connects to multiple DUs (e.g., 10 DUs), and each DU may connect to multiple RRUs (e.g., 18 RRUs).
  • a single CU may manage 10 different cell sites (or cellular base stations) and 180 different RRUs.
  • the radio access network 120 or portions of the radio access network 120 may be implemented using multi-access edge computing (MEC) that allows computing and storage resources to be moved closer to user equipment. Allowing data to be processed and stored at the edge of a network that is located close to the user equipment may be necessary to satisfy low-latency application requirements.
  • MEC multi-access edge computing
  • the DU 204 and CU-UP 216 may be executed as virtual instances within a data center environment that provides single-digit millisecond latencies (e.g., less than 2ms) from the virtual instances to the UE 108.
  • FIG. 1C depicts an embodiment of a radio access network 120 and a core network 130 for providing a communications channel (or channel) between user equipment and data network 180.
  • the core network 130 includes UPF 132 for handling user data in the core network 130.
  • Data is transported between the radio access network 120 and the core network 130 via the N3 interface.
  • the data may be tunneled across the N3 interface (e.g., IP routing may be done on the tunnel header IP address instead of using end user IP addresses). This may allow for maintaining a stable IP anchor point even though UE 108 may be moving around a network of cells or moving from one coverage area into another coverage area.
  • the UPF 132 may connect to external data networks, such as the data network 180 via the N6 interface.
  • the data may not be tunneled across the N6 interface as IP packets may be routed based on end user IP addresses.
  • the UPF 132 may connect to the SMF 133 via an N4 interface.
  • the core network 130 includes a group of control plane functions 140 comprising SMF 133, AMF 134, PCF 135, NRF 136, AF 137, and NSSF 138.
  • the SMF 133 may configure or control the UPF 132 via the N4 interface.
  • the SMF 133 may control packet forwarding rules used by the UPF 132 and adjust QoS parameters for QoS enforcement of data flows (e.g., limiting available data rates).
  • multiple SMF/UPF pairs may be used to simultaneously manage user plane traffic for a particular user device, such as UE 108.
  • a set of SMFs may be associated with UE 108, wherein each SMF of the set of SMFs corresponds with a network slice.
  • the SMF 133 may control the UPF 132 on a per end user data session basis, in which the SMF 133 may create, update, and remove session information in the UPF 132.
  • the SMF 133 may select an appropriate UPF for a user plane path by querying the NRF 136 to identify a list of available UPFs and their corresponding capabilities and locations.
  • the SMF 133 may select the UPF 132 based on a physical location of the UE 108 and a physical location of the UPF 132 (e.g., corresponding with a physical location of a data center in which the UPF 132 is running).
  • the SMF 133 may also select the UPF 132 based on a particular network slice supported by the UPF 132 or based on a particular data network that is connected to the UPF 132.
  • the ability to query the NRF 136 for UPF information eliminates the need for the SMF 133 to store and update the UPF information for every available UPF within the core network 130.
  • the SMF 133 may query the NRF 136 to identify a set of available UPFs for a packet data unit (PDU) session and acquire UPF information from a variety of sources, such as the AMF 134 or the UE 108.
  • the UPF information may include a location of the UPF 132, a location of the UE 108, the UPF’s dynamic load, the UPF’s static capacity among UPFs supporting the same data network, and the capability of the UPF 132.
  • the radio access network 120 may provide separation of the centralized unit for the control plane (CU-CP) 214 and the centralized unit for the user plane (CU-UP) 216 functionalities while supporting network slicing.
  • the CU-CP 214 may obtain resource utilization and latency information from the DU 204 and/or the CU-UP 216, and select a CU-UP to pair with the DU 204 based on the resource utilization and latency information in order to configure a network slice.
  • Network slice configuration information associated with the network slice may be provided to the UE 108 for purposes of initiating communication with the UPF 132 using the network slice.
  • FIG. 2A depicts an embodiment of a radio access network 120.
  • the radio access network 120 includes virtualized CU units 220, virtualized DU units 210, remote radio units (RRUs) 202, and a RAN intelligent controller (RIC) 230.
  • the virtualized DU units 210 may comprise virtualized versions of distributed units (DUs) 204.
  • the distributed unit (DU) 204 may comprise a logical node configured to provide functions for the radio link control (RLC) layer, the medium access control (MAC) layer, and the physical layer (PHY) layers.
  • RLC radio link control
  • MAC medium access control
  • PHY physical layer
  • the virtualized CU units 220 may comprise virtualized versions of centralized units (CUs) comprising a centralized unit for the user plane CU-UP 216 and a centralized unit for the control plane CU-CP 214.
  • the centralized units (CUs) may comprise a logical node configured to provide functions for the radio resource control (RRC) layer, the packet data convergence control (PDCP) layer, and the service data adaptation protocol (SDAP) layer.
  • RRC radio resource control
  • PDCP packet data convergence control
  • SDAP service data adaptation protocol
  • the centralized unit for the control plane CU-CP 214 may comprise a logical node configured to provide functions of the control plane part of the RRC and PDCP.
  • the centralized unit for the user plane CU-UP 216 may comprise a logical node configured to provide functions of the user plane part of the SDAP and PDCP. Virtualizing the control plane and user plane functions allows the centralized units (CUs) to be consolidated in one or more data centers on RAN-based open interfaces.
  • the remote radio units (RRUs) 202 may correspond with different cell sites.
  • a single DU may connect to multiple RRUs via a fronthaul interface 203.
  • the fronthaul interface 203 may provide connectivity between DUs and RRUs.
  • DU 204a may connect to 18 RRUs via the fronthaul interface 203.
  • Centralized units (CUs) may control the operation of multiple DUs via a midhaul Fl interface that comprises the Fl-C and Fl-U interfaces.
  • the Fl interface may support control plane and user plane separation, and separate the Radio Network Layer and the Transport Network Layer.
  • the centralized unit for the control plane CU-CP 214 may connect to ten different DUs within the virtualized DU units 210.
  • the centralized unit for the control plane CU-CP 214 may control ten DUs and 180 RRUs.
  • a single distributed unit (DU) 204 may be located at a cell site or in a local data center. Centralizing the distributed unit (DU) 204 at a local data center or at a single cell site location instead of distributing the DU 204 across multiple cell sites may result in reduced implementation costs.
  • the centralized unit for the control plane CU-CP 214 may host the radio resource control (RRC) layer and the control plane part of the packet data convergence control (PDCP) layer.
  • the El interface may separate the Radio Network Layer and the Transport Network Layer.
  • the CU-CP 214 terminates the El interface connected with the centralized unit for the user plane CU-UP 216 and the Fl-C interface connected with the distributed units (DUs) 204.
  • the centralized unit for the user plane CU-UP 216 hosts the user plane part of the packet data convergence control (PDCP) layer and the service data adaptation protocol (SDAP) layer.
  • the CU-UP 216 terminates the El interface connected with the centralized unit for the control plane CU-CP 214 and the Fl-U interface connected with the distributed units (DUs) 204.
  • the distributed units (DUs) 204 may handle the lower layers of the baseband processing up through the packet data convergence control (PDCP) layer of the protocol stack.
  • the interfaces Fl-C and El may carry signaling information for setting up, modifying, relocating, and/or releasing a UE context.
  • the RAN intelligent controller (RIC) 230 may control the underlying RAN elements via the E2 interface.
  • the E2 interface connects the RAN intelligent controller (RIC) 230 to the distributed units (DUs) 204 and the centralized units CU-CP 214 and CU-UP 216.
  • the RAN intelligent controller (RIC) 230 may comprise a near-real time RIC.
  • a non-real-time RIC may comprise a logical node allowing non-real time control rather than near-real-time control and the near-real-time RIC 230 may comprise a logical node allowing near-real-time control and optimization of RAN elements and resources on the bases of information collected from the distributed units (DUs) 204 and the centralized units CU-CP 214 and CU-UP 216 via the E2 interface.
  • DUs distributed units
  • CU-CP 214 and CU-UP 216 via the E2 interface.
  • both a distributed unit (DU) 204 and a corresponding centralized unit CU-UP 216 may be implemented at a cell site.
  • a distributed unit (DU) 204 may be implemented at a cell site and the corresponding centralized unit CU-UP 216 may be implemented at a local data center (LDC).
  • both a distributed unit (DU) 204 and a corresponding centralized unit CU-UP 216 may be implemented at a local data center (LDC).
  • both a distributed unit (DU) 204 and a corresponding centralized unit CU-UP 216 may be implemented at a cell site, but the corresponding centralized unit CU-CP 214 may be implemented at a local data center (LDC).
  • a distributed unit (DU) 204 may be implemented at a local data center (LDC) and the corresponding centralized units CU-CP 214 and CU-UP 216 may be implemented at an edge data center (EDC).
  • EDC edge data center
  • network slicing operations may be communicated via the El, Fl-C, and Fl -U interfaces of the radio access network 120.
  • CU-CP 214 may select the appropriate DU 204 and CU-UP 216 entities to serve a network slicing request associated with a particular service level agreement (SLA).
  • SLA service level agreement
  • FIG. 2B depicts another embodiment of a radio access network 120.
  • the radio access network 120 includes hardware-level components and software-level components.
  • the hardware-level components include one or more processors 270, one or more memory 271, and one or more disks 272.
  • the software-level components include software applications, such as a RAN intelligent controller (RIC) 230, virtualized CU unit (VCU) 220, and virtualized DU unit (VDU) 210.
  • the software-level components also include an ADNA Manager 282 for orchestrating the execution of various RAN processes, such as the RIC 230, VCU 220, and VDU 210 using a pool of ADNAs.
  • RIC RAN intelligent controller
  • VCU virtualized CU unit
  • VDU virtualized DU unit
  • the ADNA Manager 282 may initiate a RAN process by identifying an initiator ADNA for the RAN process within the pool of ADNAs and invoking the initiator ADNA. Over time, the ADNA Manager 282 may add, remove, or update ADNAs.
  • the software-level components may be run using the hardware-level components or executed using processor and storage components of the hardware-level components. In one example, one or more of the RIC 230, VCU 220, and VDU 210 may be run using the processor 270, memory 271, and disk 272. In another example, one or more of the RIC 230, VCU 220, and VDU 210 may be run using a virtual processor and a virtual memory that are themselves executed or generated using the processor 270, memory 271, and disk 272.
  • the software-level components also include virtualization layer processes, such as virtual machine 273, hypervisor 274, container engine 275, and host operating system 276.
  • the hypervisor 274 may comprise a native hypervisor (or bare-metal hypervisor) or a hosted hypervisor (or type 2 hypervisor).
  • the hypervisor 274 may provide a virtual operating platform for running one or more virtual machines, such as virtual machine 273.
  • a hypervisor may comprise software that creates and runs virtual machine instances.
  • Virtual machine 273 may include a plurality of virtual hardware devices, such as a virtual processor, a virtual memory, and a virtual disk.
  • the virtual machine 273 may include a guest operating system that has the capability to run one or more software applications, such as the RAN intelligent controller (RIC) 230.
  • the virtual machine 273 may run the host operation system 276 upon which the container engine 275 may run.
  • a virtual machine, such as virtual machine 273, may include one or more virtual processors.
  • a container engine 275 may run on top of the host operating system 276 in order to run multiple isolated instances (or containers) on the same operating system kernel of the host operating system 276.
  • Containers may perform virtualization at the operating system level and may provide a virtualized environment for running applications and their dependencies.
  • the container engine 275 may acquire a container image and convert the container image into running processes.
  • the container engine 275 may group containers that make up an application into logical units (or pods).
  • a pod may contain one or more containers and all containers in a pod may run on the same node in a cluster. Each pod may serve as a deployment unit for the cluster. Each pod may run a single instance of an application.
  • a “replica” may refer to a unit of replication employed by a computing platform to provision or deprovision resources. Some computing platforms may run containers directly and therefore a container may comprise the unit of replication. Other computing platforms may wrap one or more containers into a pod and therefore a pod may comprise the unit of replication.
  • a replication controller may be used to ensure that a specified number of replicas of a pod are running at the same time. If less than the specified number of pods are running (e.g., due to a node failure or pod termination), then the replication controller may automatically replace a failed pod with a new pod.
  • the number of replicas may be dynamically adjusted based on a prior number of node failures. For example, if it is detected that a prior number of node failures for nodes in a cluster running a particular network slice has exceeded a threshold number of node failures, then the specified number of replicas may be increased (e.g., increased by one). Running multiple pod instances and keeping the specified number of replicas constant may prevent users from losing access to their application in the event that a particular pod fails or becomes inaccessible.
  • a virtualized infrastructure manager not depicted may run on the radio access network (RAN) 120 in order to provide a centralized platform for managing a virtualized infrastructure for deploying various components of the radio access network (RAN) 120.
  • the virtualized infrastructure manager may manage the provisioning of virtual machines, containers, and pods.
  • the virtualized infrastructure manager may also manage a replication controller responsible for managing a number of pods.
  • the virtualized infrastructure manager may perform various virtualized infrastructure related tasks, such as cloning virtual machines, creating new virtual machines, monitoring the state of virtual machines, and facilitating backups of virtual machines.
  • FIG. 2C depicts an embodiment of the radio access network 120 of Figure 2B in which the virtualization layer includes a containerized environment 279.
  • the containerized environment 279 includes a container engine 275 for instantiating and managing application containers, such as container 277.
  • Containerized applications may comprise applications that run in isolated runtime environments (or containers).
  • the containerized environment 279 may include a container orchestration service for automating the deployments of containerized applications.
  • the container 277 may be used to deploy microservices for running network functions.
  • the container 277 may run DU components and/or CU components of the radio access network (RAN) 120.
  • the containerized environment 279 may be executed using hardware-level components or executed using processor and storage components of the hardware-level components.
  • the containerized environment 279 may be run using the processor 270, memory 271, and disk 272. In another example, the containerized environment 279 may be run using a virtual processor and a virtual memory that are themselves executed or generated using the processor 270, memory 271, and disk 272.
  • FIG. 2D depicts an embodiment of a core network 130.
  • the core network 130 includes implementation for core network functions UPF 132, SMF 133, and AMF 134.
  • the core network 130 may be used to provide Internet access for user equipment via a radio access network, such as the radio access network 120 in Figure 1C.
  • the AMF 134 may be configured to host various functions including SMF selection 252 and network slicing support 254.
  • the UPF 132 may be configured to host various functions including mobility anchoring 244, packet data unit (PDU) handling 242, and QoS handling for the user plane.
  • the SMF 133 may be configured to host various functions including UE IP address allocation and management 248, selection and control of user plane functions, and PDU session control 246.
  • the core network functions may be run using containers within the containerized environment 279 that includes a container engine 275 for instantiating and managing application containers, such as container 277.
  • the containerized environment 279 may be executed or generated using a plurality of machines as depicted in Figure 2D or may be executed or generated using hardware-level components, such as the processor 270, memory 271, and disk 272 depicted in Figure 2C.
  • the software-level components also include an ADNA Manager 283 for managing the various core network processes, such as the UPF 132, SMF 133, and AMF 134 using a pool of multiple ADNAs.
  • the ADNA Manager 283 will be aware of an Initiator ADNA having been triggered by an external source from a pool of ADNAs. Over time ADNA Manager 283 may add, remove, or update ADNAs.
  • the ADNA Manager may also repair an ADNA by automatically updating one of more workflow rules for the ADNA that needs repair. This repair could also be updating of the next action table for the ADNA in question.
  • a new ADNA may be added to the pool of ADNAs and that ADNA Manager 283 may update a next action table for the repaired ADNA to point to the new ADNA.
  • the ADNA Manager 283 may add a new workflow rule to the one or more workflow rules for the repaired ADNA based on a number of exception ADNAs invoked by the repaired ADNA.
  • Figure 2E depicts an embodiment of a containerized environment 279 that includes a container engine 275 running on top of a host operating system 276.
  • the container engine 275 may manage or run containers 277 on the same operating system kernel of the host operating system 276.
  • the container engine 275 may acquire a container image and convert the container image into one or more running processes.
  • the container engine 275 may group containers that make up an application into logical units (or pods).
  • a pod may contain one or more containers and all containers in a pod may run on the same node in a cluster.
  • Each container 277 may include application code 278 and application dependencies 267, such as operating system libraries, required to run the application code 278.
  • Containers allow portability by encapsulating an application within a single executable package of software that bundles application code 278 together with the related configuration files, binaries, libraries, and dependencies required to run the application code 278.
  • an ADNA Manager 284 shown in Figure 2E is run using the containerized environment 279.
  • the ADNA Manager 284 operates as a container that is aware of all the different ADNAs housed within it.
  • the ADNA Manager 284 manages various functions as needed, such as lifecyle, resource allocation, recycling, and the like.
  • the ADNA Manager 284 houses the functions of being intelligent of the operations statistics of all ADNAs contained within it.
  • FIG. 3 A depicts one embodiment of a microservices orchestration for performing a process.
  • the microservices orchestration may be represented using a business process model and notation (BPMN) graphical model.
  • BPMN business process model and notation
  • the process requires three subprocesses 304, 306, and 308 to be performed.
  • the subprocesses may be determined before runtime of the process such that prior to the execution of the subprocess 304, the total number of subprocesses for the process is known and is three subprocesses.
  • the top-level orchestration is fixed prior to execution of the subprocess 304.
  • the subprocess 304 may interact with an API 305 providing a request 310 to the API 305 and receiving a response 312 from the API 305.
  • Each of the subprocesses 304, 306, and 308 may communicate with the corresponding API 305, 307, and 309 using a request-response pattern.
  • subprocess 304 may send a request message, such as request 310, to the API 305 and then wait for a corresponding response message, such as response 312, before execution of the next subprocess 306.
  • request message such as request 310
  • response 312 a response message
  • an ADNA task block does not require a request-response pattern prior to invoking another ADNA.
  • FIG. 3B depicts one embodiment of a process performed using a plurality of atomic deterministic next action (ADNA) task blocks.
  • ADNA atomic deterministic next action
  • the process is performed using three ADNAs 320, 322, and 324 that individually do not require a request-response pattern prior to invoking a subsequent ADNA.
  • the top-level orchestration is variable and not fixed prior to execution of the initiator ADNA 320, the total number of ADNA task blocks used to execute the process may vary over time.
  • the dotted arrow between the ADNAs 320, 322, and 324 is used to represent that other ADNAs may be invoked during execution of the process.
  • an ADNA Prior to invoking a subsequent ADNA (or a next action ADNA), an ADNA may store data associated with an identification of the ADNA, an identification of the subsequent ADNA to be invoked, a timestamp associated with a time at which the subsequent ADNA was invoked, and one or more parameters used by the ADNA within a shared persistence layer (e.g., within persistent storage), such as persistence layer 326.
  • the data written to the shared persistence layer may comprise breadcrumbs 327.
  • the breadcrumbs 327 may be accessed by a first ADNA in order to identify which ADNA invoked the first ADNA. In some cases, the first ADNA may access the breadcrumbs 327 to determine the error or out of range data parameter that leads to the first ADNA being invoked.
  • FIG. 3C depicts one embodiment of an ADNA 322.
  • the ADNA 322 includes an API 331, workflow rules 330, and next actions 333.
  • the API 331 may provide an interface for invoking the ADNA 322.
  • the API 331 may require a specified set of inputs.
  • the workflow rules 330 may include program code for one or more workflow rules.
  • the next actions 333 may include a mapping table for mapping a function outcome from the workflow rules 330 to a subsequent ADNA to be invoked by the ADNA 322.
  • the next actions 333 may map the outcomes or results from the application of the one or more workflow rules to one or more next action ADNAs in the case that the generated outcomes and results meet predetermined criteria or to one or more exception ADNAs in the case that the generated outcomes and results do not meet the predetermined criteria e.g., a function outcome may not meet the predetermined criteria if a numerical value for the function outcome is greater than a maximum threshold value or less than a minimum threshold value).
  • the workflow rules 330 may be executed to implement one or more specific tasks to be performed by the ADNA 322.
  • the ADNA 322 may acquire a set of input parameters from the API 331 and execute qualification rules to determine whether the set of input parameters satisfies the qualification rules.
  • the qualification rules may require that each of the set of input parameters is of a particular type (e.g., a character string or a floating-point number), that each of the set of input parameters is within a particular range (e.g., between a minimum and maximum value), and that at least a threshold number of input parameters have been passed to the ADNA 322 via the API 331. If the qualification rules are satisfied, then an ADNA function may be executed using the set of input parameters.
  • the ADNA 322 may determine a subsequent ADNA to be invoked based on an outcome of the ADNA function. Prior to invoking the subsequent ADNA, breadcrumbs including an identification of the ADNA 322, an identification of the subsequent ADNA to be invoked, the set of input parameters, the outcome of the ADNA function, and a timestamp associated with a time at which the subsequent ADNA was invoked by the ADNA 322 is written to a persistence layer.
  • a process specified by the workflow rules 330 may be performed using one or more real machines, one or more virtual machines, and/or one or more containerized applications.
  • the process specified by the workflow rules 330 may be performed using a containerized environment, such as the containerized environment 279 in Figure 2E.
  • the process specified by the workflow rules 330 tests one or more qualifications rules on input parameters passed to the ADNA 322, executes an ADNA function using the input parameters, determines a subsequent ADNA to be invoked based on an outcome of the ADNA function, writes breadcrumb information to a shared persistence layer, and invokes the subsequent ADNA after the breadcrumb information has been stored within the shared persistence layer.
  • the process specified by the workflow rules 330 in Figure 3C includes operations 332, 334, 336, 338, and 340. In operation 332, qualification rules are applied to and executed to ensure that the required input parameters have been received by the ADNA 322 via the API 331.
  • operation 334 it is determined whether the qualification rules for the input parameters have been satisfied and if the qualification rules have been satisfied, then additional processing is performed such as executing an ADNA function. If it is determined that one or more of the qualification rules for the input parameters have not been satisfied, then an exception ADNA may be invoked based on the one or more qualification rules that were not satisfied. For example, if a second input parameter is not within a valid range, then the exception ADNA 352 referenced by the mapping table entry Exception_2 that corresponds with an out of range second input parameter may be invoked by the ADNA 322. In operation 336, a subsequent ADNA (or a next action ADNA) is determined based on an outcome of the ADNA function.
  • next action ADNA 324 referenced by NextAction_l may be invoked; otherwise, if the outcome of the ADNA function is not greater than the threshold value, then the next action ADNA 324 referenced by NextAction_2 may be invoked.
  • mapping entry for NextAction_l is to ADNA 324 and the mapping entry for NextAction_2 is to ADNA 324'.
  • mapping entries of the next actions 333 may be adjusted to reference other ADNAs.
  • a next action ADNA or an exception ADNA is invoked or triggered based on the function outcome.
  • breadcrumb information including an identification of the ADNA 322 and the input parameters passed to the ADNA 322 may be stored using a shared persistence layer, such as persistent layer 326.
  • Figure 3D depicts one embodiment of the process performed in Figure 3B in which an exception ADNA was invoked.
  • an exception condition occurred that triggered invocation of the exception ADNA 352.
  • an exception ADNA may be invoked when an input parameter is out of range or an outcome of an ADNA function is out of range (e.g., the ADNA function generates a value that is greater than a maximum threshold value).
  • the exception ADNA 352 may acquire breadcrumb information associated with the ADNA invoking the exception ADNA 352 from the persistence layer 326.
  • the breadcrumb information may include an identification of the ADNA 322 that invoked (or called) the exception ADNA 352 at a particular time.
  • the breadcrumb information may also be used to identify an input parameter that was out of range or a set of input parameters that led to the outcome of the ADNA function from being out of range.
  • the exception ADNA 352 may acquire an updated input parameter or compute the updated input parameter so that the input parameter satisfies the qualification rules required by the invoking ADNA. After the exception ADNA 352 has obtained the updated input parameter, then the exception ADNA 352 may invoke the ADNA 324 with the updated input parameter.
  • Figure 3E depicts one embodiment of the process performed in Figure 3B in which an ADNA has been updated to reference a new ADNA that has been added to a pool of ADNAs.
  • the ADNA 322 has been updated to reference the new ADNA 344 and the updated ADNA 322 now invokes ADNA 344.
  • the new ADNA 344 may be associated with a new hardware device or a new virtual device added to a system.
  • the new ADNA 344 may correspond with a newly instantiated virtualized network function.
  • the new ADNA 344 may be automatically created and added to the pool of ADNAs in response to detection that the ADNA 322 had invoked the exception ADNA 352 more than a threshold number of times (e.g., more than ten times).
  • Figure 3F depicts one embodiment of an updated ADNA 322 in which the NextAction_2 mapping for the ADNA 322 of Figure 3C has been changed to reference the new ADNA 344.
  • Figure 3E depicts the updated ADNA 322 invoking ADNA 344 as the next action ADNA.
  • Figure 3G depicts one embodiment of an exception ADNA 352.
  • the exception ADNA 352 includes an API 361 and exception rules 360.
  • the API 361 may provide an interface for invoking the exception ADNA 352.
  • the API 361 may require one or more input parameters of a particular data type (e.g., a character string or a floating point number).
  • the exception rules 360 may include program code for one or more exception rules.
  • the exception rules 360 may be executed to implement one or more specific tasks to be performed by the exception ADNA 352.
  • the exception ADNA 352 may acquire a set of input parameters from the API 361 and execute qualification rules to determine whether the set of input parameters satisfies the qualification rules.
  • the qualification rules may require that each of the set of input parameters is of a particular type (e.g., a character string, an integer, or a floating-point number), that each of the set of input parameters is within a particular range (e.g., between a minimum and maximum value), and that at least a threshold number of input parameters have been passed to the exception ADNA 352 via the API 361. If the qualification rules are satisfied, then the exception ADNA 352 may identify the ADNA that invoked the exception ADNA 352 and determine an input parameter or function outcome responsible for causing the exception ADNA 352 to be invoked. The exception ADNA 352 may acquire breadcrumb information from a persistence layer to determine the input parameter or function outcome responsible for causing the exception ADNA 352 to be invoked.
  • a particular type e.g., a character string, an integer, or a floating-point number
  • a particular range e.g., between a minimum and maximum value
  • data associated with the input parameter or function outcome may be remediated.
  • the data associated with the input parameter or function outcome may be reacquired from the original source of the data or may be acquired from a different data source.
  • a next action ADNA may be determined based on breadcrumb information stored within the persistence layer.
  • the breadcrumb information may include an identification of the ADNA that invoked the exception ADNA 352.
  • a process specified by the exception rules 360 may be performed using one or more real machines, one or more virtual machines, and/or one or more containerized applications.
  • the process specified by the exception rules 360 may be performed using a containerized environment, such as the containerized environment 279 in Figure 2E.
  • qualification rules are applied to and executed to ensure that the input parameters received by the exception ADNA 352 via the API 361 are valid or within an acceptable range of values.
  • a subsequent ADNA (or a next action ADNA) is determined based on an identification of the ADNA that invoked the exception ADNA 352.
  • breadcrumb information including an identification of the exception ADNA 352, input parameters passed to the exception ADNA 352, and an identification of the data remediated by the exception ADNA 352 may be stored using a shared persistence layer.
  • next action ADNA may be invoked.
  • a repair ADNA may be invoked if the exception ADNA 352 has been invoked more than a threshold number of times by a particular ADNA in order to repair the ADNA that invoked the exception ADNA 352.
  • a machine learning engine may access the shared persistence layer, such as the persistence layer 326 in Figure 3D, to identify a set of ADNAs to be repaired.
  • the set of ADNAs to be repaired may comprise the top one hundred ADNAs that invoked the greatest number of exception ADNAs.
  • At least one embodiment of the disclosed technology includes one or more processors configured to identify a first atomic deterministic next action task block out of a pool of atomic deterministic next action task blocks associated with a workflow process, acquire a set of input parameters for the first atomic deterministic next action task block, detect that a first input parameter of the set of input parameters does not satisfy a qualification rule for the first atomic deterministic next action task block, identify an exception atomic deterministic next action task block in response to detection that the first input parameter does not satisfy the qualification rule, store breadcrumb information for the first atomic deterministic next action task block within a persistence layer prior to the exception atomic deterministic next action task block being invoked, and invoke the exception atomic deterministic next action task block.
  • Figure 4 is a logic diagram showing a method for providing an updated atomic deterministic next action system.
  • the method identifies a first atomic deterministic next action task block out of a pool of atomic deterministic next action task blocks associated with one or more workflow rules.
  • the method detects that a first input parameter does not satisfy a qualification rule for the first atomic deterministic next action task block.
  • the method identifies and invokes an exception atomic deterministic next action task block, in response to detection that the first input parameter does not satisfy the qualification rule.
  • the method detects that an exception atomic deterministic next action task block has been invoked more than a threshold number of times.
  • the method repairs the first atomic deterministic next action task block in response to detection that the exception atomic deterministic next action task block has been invoked more than the threshold number of times.
  • the method adds a new atomic deterministic next action task block to the pool of atomic deterministic next action task blocks.
  • the method adds a new workflow rule to the one or more workflow rules for the repaired first atomic deterministic next action task block based on a number of exception atomic deterministic next action task blocks invoked by the repaired atomic deterministic next action task block.
  • the method invokes the new atomic deterministic next action task block using the repaired first atomic deterministic next action task block.
  • Figure 5 shows a system diagram that describes an example implementation of a computing system(s) for implementing embodiments described herein.
  • the functionality described herein for an updated atomic deterministic next action system can be implemented either on dedicated hardware, as a software instance running on dedicated hardware, or as a virtualized function instantiated on an appropriate platform, e.g., a cloud infrastructure.
  • an appropriate platform e.g., a cloud infrastructure.
  • such functionality may be completely software-based and designed as cloudnative, meaning that they’re agnostic to the underlying cloud infrastructure, allowing higher deployment agility and flexibility.
  • host computer system(s) 501 may represent those in various data centers and cell sites shown and/or described herein that host the functions, components, microservices and other aspects described herein to implement an updated atomic deterministic next action system.
  • one or more special-purpose computing systems may be used to implement the functionality described herein.
  • various embodiments described herein may be implemented in software, hardware, firmware, or in some combination thereof.
  • Host computer system(s) 501 may include memory 502, one or more central processing units (CPUs) 514, I/O interfaces 518, other computer-readable media 520, and network connections 522.
  • CPUs central processing units
  • Memory 502 may include one or more various types of non-volatile and/or volatile storage technologies. Examples of memory 502 may include, but are not limited to, flash memory, hard disk drives, optical drives, solid-state drives, various types of random-access memory (RAM), various types of read-only memory (ROM), other computer-readable storage media (also referred to as processor-readable storage media), or the like, or any combination thereof. Memory 502 may be utilized to store information, including computer-readable instructions that are utilized by CPU 514 to perform actions, including those of embodiments described herein.
  • Memory 502 may have stored thereon control module(s) 504.
  • the control module(s) 504 may be configured to implement and/or perform some or all of the functions of the systems, components and modules described herein for an updated atomic deterministic next action system.
  • Memory 502 may also store other programs and data 510, which may include rules, databases, application programming interfaces (APIs), software platforms, cloud computing service software, network management software, network orchestrator software, network functions (NF), Al or ML programs or models to perform the functionality described herein, user interfaces, operating systems, other network management functions, other NFs, etc.
  • APIs application programming interfaces
  • NF network functions
  • Al or ML programs or models to perform the functionality described herein, user interfaces, operating systems, other network management functions, other NFs, etc.
  • Network connections 522 are configured to communicate with other computing devices to facilitate the functionality described herein.
  • the network connections 522 include transmitters and receivers (not illustrated), cellular telecommunication network equipment and interfaces, and/or other computer network equipment and interfaces to send and receive data as described herein, such as to send and receive instructions, commands and data to implement the processes described herein.
  • I/O interfaces 518 may include a video interface, other data input or output interfaces, or the like.
  • Other computer-readable media 520 may include other types of stationary or removable computer-readable media, such as removable flash drives, external hard drives, or the like.
  • the term “based on” may be read as “based at least in part on.”
  • use of numerical terms such as a “first” object, a “second” object, and a “third” object may not imply an ordering of objects, but may instead be used for identification purposes to identify or distinguish separate objects.
  • the term “set” of objects may refer to a “set” of one or more of the objects.
  • each operation in a flowchart may correspond with a program module or portion of computer program code, which may comprise one or more computer-executable instructions for implementing the specified functionality.
  • the functionality noted within an operation may occur out of the order noted in the figures. For example, two operations shown in succession may, in fact, be executed substantially concurrently, or the operations may sometimes be executed in the reverse order, depending upon the functionality involved. In some implementations, operations may be omitted and other operations added without departing from the spirit and scope of the present subject matter.
  • the functionality noted within an operation may be implemented using hardware, software, or a combination of hardware and software.
  • the hardware may include microcontrollers, microprocessors, field programmable gate arrays (FPGAs), and electronic circuitry.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Strategic Management (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Educational Administration (AREA)
  • Game Theory and Decision Science (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Development Economics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Computer Hardware Design (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention concerne des systèmes et des procédés pour fournir un gestionnaire d'ADNA dynamiquement extensible avec une prise de décision atomique décentralisée. La prise de décision atomique décentralisée peut être effectuée à l'aide de blocs de tâches d'action suivante déterministe atomique (ADNA) qui exécutent une ou plusieurs règles de flux de travail et invoquent ensuite une ou plusieurs ADNA dans un groupe d'ADNA. Le gestionnaire d'ADNA peut identifier un premier bloc de tâches d'ADNA, déterminer un ensemble de paramètres d'entrée pour le premier bloc de tâches d'ADNA, détecter qu'un premier paramètre d'entrée de l'ensemble de paramètres d'entrée ne satisfait pas une règle de qualification pour le premier bloc de tâches d'ADNA, identifier un bloc de tâches d'ADNA d'exception en réponse à la détection du fait que le premier paramètre d'entrée ne satisfait pas la règle de qualification, stocker des informations de piste de navigation pour le premier bloc de tâches d'ADNA dans une couche de persistance avant que le bloc de tâches d'ADNA d'exception ne soit invoqué, et invoquer le bloc de tâches d'ADNA d'exception.
PCT/US2023/077478 2022-10-26 2023-10-20 Action suivante déterministe atomique WO2024091858A1 (fr)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US202263419610P 2022-10-26 2022-10-26
US202263419608P 2022-10-26 2022-10-26
US63/419,610 2022-10-26
US63/419,608 2022-10-26
US18/341,635 2023-06-26
US18/341,632 2023-06-26
US18/341,635 US20240147259A1 (en) 2022-10-26 2023-06-26 Repair atomic deterministic next action
US18/341,632 US20240143384A1 (en) 2022-10-26 2023-06-26 Atomic deterministic next action

Publications (1)

Publication Number Publication Date
WO2024091858A1 true WO2024091858A1 (fr) 2024-05-02

Family

ID=88874967

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/077478 WO2024091858A1 (fr) 2022-10-26 2023-10-20 Action suivante déterministe atomique

Country Status (1)

Country Link
WO (1) WO2024091858A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10846204B2 (en) * 2018-04-20 2020-11-24 Microsoft Technology Licensing, Llc Remediation of design time and runtime workflow errors
US11422680B1 (en) * 2014-05-22 2022-08-23 EMC IP Holding Company LLC Workflow execution framework

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11422680B1 (en) * 2014-05-22 2022-08-23 EMC IP Holding Company LLC Workflow execution framework
US10846204B2 (en) * 2018-04-20 2020-11-24 Microsoft Technology Licensing, Llc Remediation of design time and runtime workflow errors

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
FRIEDRICH G ET AL: "Exception Handling for Repair in Service-Based Processes", IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, IEEE SERVICE CENTER, LOS ALAMITOS, CA, US, vol. 36, no. 2, 5 March 2010 (2010-03-05), pages 198 - 215, XP011299918, ISSN: 0098-5589 *

Similar Documents

Publication Publication Date Title
US11039321B2 (en) Methods and systems for network slicing
CN109952796B (zh) 可共享切片实例的创建及修改
US20240048439A1 (en) Management Services for 5G Networks and Network Functions
US20190158364A1 (en) Method and Apparatus for the Specification of a Network Slice Instance and Underlying Information Model
US20150063166A1 (en) System and Method for Mobile Network Function Virtualization
AU2018345429A1 (en) Interaction between 5G and non-5G management function entities
KR20240024114A (ko) 무선 기반 네트워크를 위한 분산된 사용자 평면 기능
US11888701B1 (en) Self-healing and resiliency in radio-based networks using a community model
Zeydan et al. Service based virtual RAN architecture for next generation cellular systems
US20230033272A1 (en) Method and apparatus for dynamic and efficient load balancing in mobile communication network
US20230337047A1 (en) Utilization of replicated pods with distributed unit applications
JP7437569B2 (ja) 無線式ネットワーク向けの高可用性データ処理ネットワーク機能
US20240147261A1 (en) Updated atomic deterministic next action
US20240147259A1 (en) Repair atomic deterministic next action
CN116074896A (zh) 多接入边缘计算切片
US20240143384A1 (en) Atomic deterministic next action
WO2018082533A1 (fr) Systèmes et procédés de gestion de réseau hiérarchique
WO2024091858A1 (fr) Action suivante déterministe atomique
US20230336440A1 (en) Containerization of telecommunication network functions
US20230337125A1 (en) Dynamic virtual networks
US20230336430A1 (en) Decoupling of packet gateway control and user plane functions
US20230337046A1 (en) Utilization of virtualized distributed units at cell sites
US20230336287A1 (en) Management of redundant links
US20230336439A1 (en) Private network connections for ran distributed units
US20240195708A1 (en) Ai driven 5g network and service management solution

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23809866

Country of ref document: EP

Kind code of ref document: A1