WO2023108718A1 - Procédé et système d'attribution de ressources de spectre pour réseau de porteuses optiques collaboratif nuage-périphérie - Google Patents

Procédé et système d'attribution de ressources de spectre pour réseau de porteuses optiques collaboratif nuage-périphérie Download PDF

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WO2023108718A1
WO2023108718A1 PCT/CN2021/140060 CN2021140060W WO2023108718A1 WO 2023108718 A1 WO2023108718 A1 WO 2023108718A1 CN 2021140060 W CN2021140060 W CN 2021140060W WO 2023108718 A1 WO2023108718 A1 WO 2023108718A1
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energy consumption
computing
edge
cloud
resources
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PCT/CN2021/140060
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Chinese (zh)
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陈伯文
王守翠
梁瑞鑫
刘玲
陈虹
高明义
沈纲祥
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苏州大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present invention relates to the technical field of optical communication, and in particular to a method and system for allocating spectrum resources of a cloud-edge collaborative optical carrier network.
  • MEC Mobile Edge Computing
  • a high-performance, low-latency, and high-bandwidth telecom service environment is provided to accelerate the distribution and download of various content, services, and applications in the network, allowing consumers to enjoy higher Quality web experience.
  • the computing resources of edge servers are relatively scarce and cannot satisfy all sudden task requests.
  • Most of the existing research focuses on distributing the resources of the original cloud data center to the vicinity of the mobile terminal device, and migrating the computing tasks of the mobile device to the MEC platform to end the problem of insufficient computing power of the mobile terminal device itself.
  • the collaborative computing of mobile edge computing and cloud data centers, and the impact of different offloading strategies on system energy consumption and computing resources have not received enough attention for a long time.
  • the computing resources of the cloud computing center are abundant, but the transmission time is extended and the energy consumption is high.
  • edge computing resources are relatively scarce, so that it cannot quickly respond to the growing computing demand, therefore, in a high-load environment, the computing energy consumption of edge servers may exceed that of cloud computing.
  • the collaboration between the edge server and the cloud server is very important to reduce the energy consumption of the system.
  • the maximum delay constraint condition of service request improves network resource utilization and reduces system energy consumption.
  • the purpose of the present invention is to provide a method and system for allocating spectrum resources of a cloud-edge collaborative optical carrier network, which can effectively balance the relationship between resources and service energy consumption, and reduce network energy consumption to the greatest extent under the condition of rationally allocating resources.
  • the present invention provides a method for allocating spectrum resources of cloud-edge cooperative optical bearer network, including the following steps:
  • S2 Generate user requests, and select processing nodes in the topology according to the computing resources required by the user requests and the maximum delay limit;
  • S3 Calculate the total energy consumption of each path from the user request to the processing node
  • S4 Select the path with the minimum total energy consumption and the free spectrum slot satisfying the constraints of spectrum consistency and continuity as the working path and allocate spectrum resources.
  • the cloud-edge collaborative optical network topology includes network nodes and user requests
  • the network nodes include base stations, switches, edge computing servers, and cloud data center servers
  • parameters include computing resources of each network node and meet the maximum delay constraints.
  • the user request includes spectrum slots, computing resources, data size and maximum delay limit required by the user request; selecting a processing node specifically includes:
  • the total energy consumption includes sending energy consumption and the edge server in the local area. Calculate energy consumption
  • the user request is migrated to the same switch in the local area
  • the total energy consumption includes transmission energy consumption, node energy consumption passing through network nodes, and computing energy consumption of adjacent edge servers;
  • the user request is sent to the cloud data center server through the switch for data processing.
  • the total energy consumption includes sending energy consumption, node energy consumption and Computational energy consumption of cloud data center servers.
  • the K shortest path algorithm is used to calculate the working path from the user request to the processing node, and K candidate paths are calculated as the routing selection, and according to the energy consumption generated by each path and the idle spectrum slot Define the priority of the candidate path, select the working path according to the priority, and define the formula of priority:
  • S k and E k respectively represent the idle spectrum slot of the kth working path and the energy consumption generated
  • S max and S min represent the maximum and minimum values of the idle spectrum slot of K working paths
  • E max and E min Indicates the maximum value and minimum value of energy consumption of K working paths
  • the user requests to calculate the total energy consumption according to the selected processing node, the total energy consumption includes sending energy consumption SEE, node energy consumption NOE, edge computing server computing energy consumption CAE e and cloud data center server computing energy consumption CAE c , namely:
  • p u represents the transmission power of the user equipment
  • d u represents the size of the task
  • p j represents the port power of the switch j
  • v j represents the forwarding rate of the switch
  • n represents the number of nodes the path passes through
  • ⁇ e represents the edge computing server’s real-time processing capability
  • ⁇ c indicates the processing capacity allocated to each task by the cloud data center computing server
  • lu u indicates the number of computing resources requested by the user
  • the user requests u to transmit the uplink transmission rate of base station b through the wireless sub-channel:
  • W represents the channel bandwidth
  • p u represents the transmission power of the user equipment
  • ⁇ 2 represents the noise power.
  • it also includes the step of: after the spectrum resources are successfully allocated, update the computing resources in real time on the processing node processing the user request;
  • the spectrum resources occupied by the working path are released, and at the same time, the computing resources of the processing node processing the user request are released;
  • a system for allocating spectrum resources of a cloud-edge collaborative optical network comprising:
  • Cloud-edge collaborative optical network including topology, is used to provide computing resources
  • the user request module is used to generate user requests, and select processing nodes in the topology according to the computing resources required by the user requests and the maximum delay limit;
  • the energy consumption calculation module is used to calculate the total energy consumption of each path from the user request to the processing node;
  • the path selection and resource allocation module is used to select the path with the minimum total energy consumption and the idle spectrum slot satisfying the constraints of spectrum consistency and continuity as the working path and allocate spectrum resources.
  • a network status monitoring module which is used to coordinate cloud-edge optical network, generate user requests, process node selection, work path selection, spectrum resource allocation, calculation update, resource release and energy consumption calculation monitoring and judgment.
  • the present invention mainly aims at how to balance computing resources, spectrum resources, and service energy consumption, and proposes a method and system for optimizing energy consumption of the cloud-edge collaborative network optical carrier wireless network; for each user request, according to the priority level of the task
  • select the server that handles the business According to the deployment strategy of the cloud data center and mobile edge computing in the service area, select the server that handles the business; use the K shortest path algorithm to calculate the working path between the business and the edge computing server; calculate the priority of the path, and select the one with the highest priority first path to reduce the network blocking rate.
  • the spectrum allocation algorithm of the first hit to allocate spectrum resources for the path.
  • the resource status is updated in real time; after each user request is successfully established, the transmission energy consumption is calculated according to the user's transmission power, service data size and wireless sub-channel transmission rate, and according to the number of nodes passed by the path, service data size, node power and forwarding rate Calculate the energy consumption of path nodes according to the load condition, and calculate the computing energy consumption of the cloud-side collaborative optical wireless network based on the computing power of the server and the energy consumption per unit time of the server processing data; through the cloud-edge collaborative optical wireless network, the energy consumption
  • the optimization method and system can effectively balance the relationship between network resources and service energy consumption, and minimize the energy consumption of the cloud-edge collaborative optical wireless network under the condition of rational allocation of resources.
  • Fig. 1 is a schematic diagram of the overall process flow of the method of the present invention
  • Fig. 2 is a schematic flow chart of the specific implementation of the method of the present invention.
  • Fig. 3 is a schematic structural diagram of the system of the present invention.
  • Fig. 4 is a schematic diagram of a cloud-edge collaborative optical network architecture according to an embodiment of the present invention.
  • the present invention provides a method for allocating spectrum resources in a cloud-edge cooperative optical bearer network, including the following steps:
  • S2 Generate user requests, and select processing nodes in the topology according to the computing resources required by the user requests and the maximum delay limit;
  • S3 Calculate the total energy consumption of each path from the user request to the processing node
  • S4 Select the path with the minimum total energy consumption and the free spectrum slot satisfying the constraints of spectrum consistency and continuity as the working path and allocate spectrum resources.
  • the energy consumption generated by business processing is different, and the server with less energy consumption is preferentially selected for unloading and processing, and the resource occupation status of each server is adapted.
  • three kinds of energy consumption are mainly considered, including service transmission energy consumption, node energy consumption and calculation energy consumption.
  • Service transmission energy consumption refers to the energy consumption generated when the user and the edge node in the service area send the service request to the edge server for processing through wireless transmission, which is related to the transmission power of the user equipment, the data size of the service request, and the wireless transmission rate Relevant; node energy consumption refers to the energy consumption generated by the intermediate nodes in the path during the transmission process of the business, which is related to the number of passing nodes, forwarding power and forwarding rate; computing energy consumption is related to each user's computing resource requirements and The real-time computing capability of the server is related to the unit computing energy consumption of the server. Cloud data center servers and MEC servers have different unit computing energy consumption.
  • the spectrum resource allocation method includes the following steps:
  • the computing resources of the edge computing server are initialized, and the cloud-edge collaborative optical wireless network is initialized.
  • ⁇ represents a group of user requests, B ⁇ 1,2, ...,b,...b
  • ⁇ represents a group of base stations, J ⁇ b+1,b+2,...,j,...j
  • ⁇ represents the location that a group of edge computing servers can choose.
  • a cloud computing data center server
  • system energy consumption includes user request sending energy consumption, node energy consumption generated when passing network nodes such as switches, and computing energy consumption of adjacent edge servers;
  • the computing resources on the local and adjacent edge computing area servers cannot meet the computing resources or service quality required by the user request, determine whether the cloud data center server has sufficient computing resources and whether it meets the maximum delay requirement. If the cloud server has sufficient computing resources and meets the delay requirements, the user request is sent to the cloud data center server through the switch for data processing. At this time, the system energy consumption includes the transmission energy consumption of the user request, the energy consumption of the node and the computing energy consumption of the cloud server;
  • the K shortest path algorithm calculates K candidate paths as routing options, and defines the priority of the candidate paths according to the energy consumption generated by each path and the idle spectrum slot. The higher the priority, the higher the selection right of the path. When a high-priority path is blocked on a certain link, the lower-priority paths are sequentially selected for spectrum resource allocation until resources are allocated successfully or all paths are blocked.
  • the priority definition formula is as formula (1):
  • S k and E k respectively represent the idle spectrum slot of the kth working path and the energy consumption generated
  • S max and S min represent the maximum and minimum values of the idle spectrum slot of K working paths
  • E max and E min Indicates the maximum value and minimum value of energy consumption of K working paths
  • p u represents the transmission power of the user equipment
  • d u represents the size of the task
  • p j represents the port power of the switch j
  • v j represents the forwarding rate of the switch
  • n represents the number of nodes the path passes through
  • ⁇ e represents the edge computing server’s real-time processing capability
  • ⁇ c indicates the processing capacity allocated to each task by the cloud data center computing server
  • lu u indicates the number of computing resources requested by the user
  • each user request u(f,l,d,T max ) that cannot be calculated by the user equipment itself it is sent to the base station in charge of the application service in the local area.
  • the uplink transmission rate of the user request u(f,l,d,T max ) transmitted to the base station b through the wireless sub-channel is defined as formula (6) according to the Shannon formula:
  • W represents the channel bandwidth
  • p u represents the transmission power of the user equipment
  • ⁇ 2 represents the noise power.
  • the spectrum resources are allocated to the working path according to the constraints of spectrum consistency and spectrum continuity.
  • the first-hit spectrum allocation algorithm is adopted, and a spectrum resource table is generated and numbered according to the spectrum resource status of all links on the path, and the available spectrum gap is searched from the end with the smaller number. If an available spectrum gap is found, spectrum resource allocation and spectrum status update are performed; if no spectrum gap is found, spectrum allocation fails and services are blocked.
  • the present invention also provides a cloud-edge cooperative optical network resource allocation system, including:
  • Cloud-edge collaborative optical network including topology, is used to provide computing resources
  • the user request module is used to generate user requests, and select processing nodes in the topology according to the computing resources required by the user requests and the maximum delay limit;
  • the energy consumption calculation module is used to calculate the total energy consumption of each path from the user request to the processing node;
  • the path selection and resource allocation module is used to select the path with the minimum total energy consumption and the idle spectrum slot satisfying the constraints of spectrum consistency and continuity as the working path and allocate spectrum resources.
  • the cloud-edge collaborative network G configure network topology information, network connection status, number of user requests, number of edge computing servers, number of base stations and switches; user request module according to User request Generate a group of user requests, configure the number of user requests, the number of spectrum gaps required by different user requests, computing resources, data size, and maximum delay limit and other information, and the user request module selects the processing node:
  • the system also includes: a working path establishment module: according to the user request of u(f,l,d,T max ) and the server processing the request, K shortest path algorithm is used to calculate K candidates from the user request to the server path, in order to find the optimal path as the working path.
  • the energy consumption calculation module calculates the user sending energy consumption of each working path, and the path node energy consumption delay and calculates the total energy consumption.
  • the specific calculation method after each user request is successfully established Finally, record the node information passed by each user request transmission path, calculate the user transmission energy consumption and node energy consumption for each user request according to formulas (2) and (3), and then record the location of the edge computing server determined by the user request , using formula (4) or (5) to calculate the computational energy consumption of each user request.
  • the path selection and resource allocation module selects the working path with the highest priority for service processing transmission and processing according to the path priority definition formula, and according to the user request u(f,l, d, T max ) the number of spectrum gaps f required, find the bandwidth resources required to meet the user request in the selected working path, if the dual constraints of spectrum continuity and spectrum consistency are satisfied at the same time, the user request is successfully established; If the dual constraints of spectrum continuity and spectrum consistency cannot be satisfied at the same time, the establishment of the user request fails.
  • the system also includes: a computing resource update module: after the spectrum resources are successfully allocated, update the computing resources in real time to the edge computing server processing the user request;
  • Resource release module After the user request is successfully transmitted, the resource release is performed on the spectrum resources occupied by the working path, and at the same time, the computing resources of the edge computing server processing the user request are released, and finally, the information on the working path established by the user request is cleared .
  • the network status monitoring module mainly completes the initialization of cloud-edge collaborative optical wireless network, user request generation, service priority selection, edge computing server selection, working path establishment, spectrum resource allocation, computing resource update, resource release, and network energy consumption calculation state monitoring function, in order to achieve the goal of reducing system energy consumption as much as possible during the allocation of computing resources; and implement the coordination function between various modules, and the judgment and early warning function of whether each module is successfully established, and complete the reduction in mobile edge computing.
  • the target for system energy consumption is mainly completes the initialization of cloud-edge collaborative optical wireless network, user request generation, service priority selection, edge computing server selection, working path establishment, spectrum resource allocation, computing resource update, resource release, and network energy consumption calculation state monitoring function, in order to achieve the goal of reducing system energy consumption as much as possible during the allocation of computing resources; and implement the coordination function between various modules, and the judgment and early warning function of whether each module is successfully established, and complete the reduction in mobile edge computing.
  • the target for system energy consumption is mainly completes the initialization of cloud-edge collaborative optical
  • an architecture diagram of a cloud-edge collaborative optical wireless network there is a cloud computing center, two switches connected to 8 local areas, each area has a base station and a corresponding edge server, connected to the same
  • the ranges covered by different base stations on a switch are adjacent areas.
  • the computing resources of the edge computing server in the area covered by each base station are 20
  • the computing resources of the cloud data center are 1000
  • the size of business data is randomly generated in the range of 200-800Kb
  • the maximum delay is randomly generated within 0.5-1s
  • the number of computing resources the number of spectral slots and are randomly generated within a reasonable range.
  • the user request is represented by u i (f,l,d,T max ), where u i represents the user request number, f represents the number of spectrum slots required to establish a working path, l represents the computing resources required by the user request, and d represents the user requested Data size, T max indicates the maximum delay requirement of the user request.
  • Three user request sets u 1 (3,15,200,0.6), u 2 (8,10,300,0.8), and u 3 (5,30,500,1) are generated in the base station area of node 1 and node 6 in FIG. 3 .
  • the computing resources on the local and adjacent edge computing regional servers cannot meet the computing resources required by the user request, assuming that the calculated user request
  • the processing delay of offloading to the cloud data center is 0.8s, which meets the maximum delay requirement.
  • the user request needs to be transmitted through the switch node 7 to the cloud data center computing server node 9 connected to the switch, and the data transmission route is 3.
  • u 1 (3,15,200,0.6)
  • u 2 (8,10,300,0.8)
  • u 3 (5,30,500,1) need to use K shortest path routing algorithm to calculate node 1 to node 2.
  • formula (2), formula (3), formula (4) and formula (5) respectively calculate the sum of path transmission energy consumption, node energy consumption and calculation energy consumption of K paths, and calculate K paths according to formula (1) , select the path with the highest priority among the K paths as the working path.
  • the present invention regards guaranteeing the quality of network service as a prerequisite, comprehensively considers the two major factors of time delay and resources, and proposes a method and system for optimizing energy consumption of optical wireless networks with cloud-edge collaboration.
  • the system includes a remote cloud data center , the data exchange node and the MEC server deployed in the base station at the edge of the network are used as edge nodes, where the service of each edge node covers a certain range of areas, and the business requests are reasonably scheduled and migrated under the condition of satisfying the maximum delay constraint .
  • the different loads of MEC servers offload business requests to the server with the least energy consumption to minimize system energy consumption; meanwhile, it is also necessary to consider the spectrum resources in the network to further improve the service quality of the network.

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Abstract

Sont divulgués dans la présente invention un procédé et un système d'attribution de ressources de spectre pour un réseau de porteuses optiques collaboratif nuage-périphérie. Le procédé comprend les étapes suivantes : S1, lecture d'une structure topologique et de paramètres d'un réseau de porteuses optiques collaboratif en nuage ; S2, génération d'une demande d'utilisateur, et sélection d'un nœud de traitement à partir de la structure topologique selon des ressources informatiques et la limite maximale de retard requise par la demande d'utilisateur ; S3, calcul de la consommation d'énergie totale de chaque trajet de la demande d'utilisateur au nœud de traitement ; et S4, sélection, en tant que trajet de fonctionnement, du trajet qui présente la consommation d'énergie totale minimale et un intervalle de spectre inactif dont satisfait aux contraintes de cohérence et de continuité de spectre, et attribution de ressources de spectre. Au moyen de la présente invention, la relation entre les ressources et la consommation d'énergie de service est efficacement équilibrée, et la consommation d'énergie de réseau est réduite le plus possible lorsque les ressources sont attribuées de manière rationnelle.
PCT/CN2021/140060 2021-12-16 2021-12-21 Procédé et système d'attribution de ressources de spectre pour réseau de porteuses optiques collaboratif nuage-périphérie WO2023108718A1 (fr)

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