WO2023154242A1 - Systems and methods for network load balancing using machine learning - Google Patents

Systems and methods for network load balancing using machine learning Download PDF

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Publication number
WO2023154242A1
WO2023154242A1 PCT/US2023/012385 US2023012385W WO2023154242A1 WO 2023154242 A1 WO2023154242 A1 WO 2023154242A1 US 2023012385 W US2023012385 W US 2023012385W WO 2023154242 A1 WO2023154242 A1 WO 2023154242A1
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WO
WIPO (PCT)
Prior art keywords
requests
load balancing
machine learning
servers
assigned
Prior art date
Application number
PCT/US2023/012385
Other languages
French (fr)
Inventor
Brandon M. FRELICH
Nathan L. PEARCE
Original Assignee
F5, Inc.
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
Application filed by F5, Inc. filed Critical F5, Inc.
Publication of WO2023154242A1 publication Critical patent/WO2023154242A1/en

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer And Data Communications (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

Technology related to network load balancing using machine learning is disclosed. Potential imbalances in some load balancing scenarios can be addressed by using a machine learning model to generate resource utilization predictions for requests and performing load balancing operations based on the resource utilization predictions. For example, requests can be stored in a plurality of queues based on the resource utilization predictions. The queues of requests can then be processed by a load balancer. For example, the requests in the plurality of queues can be assigned to a plurality' of servers independently of one another, wherein requests from one queue are assigned to a plurality of servers without regard for the way that requests from another queue are assigned to the plurality of servers, and vice versa. Thus, in at least some scenarios, imbalances in the processing loads of the plurality of the servers can be avoided.
PCT/US2023/012385 2022-02-11 2023-02-06 Systems and methods for network load balancing using machine learning WO2023154242A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202217669910A 2022-02-11 2022-02-11
US17/669,910 2022-02-11

Publications (1)

Publication Number Publication Date
WO2023154242A1 true WO2023154242A1 (en) 2023-08-17

Family

ID=87564918

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/012385 WO2023154242A1 (en) 2022-02-11 2023-02-06 Systems and methods for network load balancing using machine learning

Country Status (1)

Country Link
WO (1) WO2023154242A1 (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050183084A1 (en) * 2004-02-13 2005-08-18 International Business Machines Corporation Autonomic workload classification using predictive assertion for wait queue and thread pool selection
US9465548B1 (en) * 2015-07-22 2016-10-11 Netapp, Inc. Methods and systems using model based techniques for determining performance capacity of a resource of a networked storage environment
US9674064B1 (en) * 2016-12-26 2017-06-06 Republic Wireless, Inc. Techniques for server transaction processing
US20200226144A1 (en) * 2019-01-10 2020-07-16 Citrix Systems, Inc. Resource scaling for distributed database services
US20200311573A1 (en) * 2019-04-01 2020-10-01 Accenture Global Solutions Limited Utilizing a machine learning model to predict a quantity of cloud resources to allocate to a customer
US10810528B1 (en) * 2019-07-19 2020-10-20 Capital One Services, Llc Identifying and utilizing the availability of enterprise resources
US20210174281A1 (en) * 2019-12-09 2021-06-10 Microsoft Technology Licensing, Llc Providing alternate resource deployment guidance for use with cloud services

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050183084A1 (en) * 2004-02-13 2005-08-18 International Business Machines Corporation Autonomic workload classification using predictive assertion for wait queue and thread pool selection
US9465548B1 (en) * 2015-07-22 2016-10-11 Netapp, Inc. Methods and systems using model based techniques for determining performance capacity of a resource of a networked storage environment
US9674064B1 (en) * 2016-12-26 2017-06-06 Republic Wireless, Inc. Techniques for server transaction processing
US20200226144A1 (en) * 2019-01-10 2020-07-16 Citrix Systems, Inc. Resource scaling for distributed database services
US20200311573A1 (en) * 2019-04-01 2020-10-01 Accenture Global Solutions Limited Utilizing a machine learning model to predict a quantity of cloud resources to allocate to a customer
US10810528B1 (en) * 2019-07-19 2020-10-20 Capital One Services, Llc Identifying and utilizing the availability of enterprise resources
US20210174281A1 (en) * 2019-12-09 2021-06-10 Microsoft Technology Licensing, Llc Providing alternate resource deployment guidance for use with cloud services

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