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Online Learning for Hierarchical Scheduling to Support Network Slicing in Cellular Networks

Published: 25 March 2022 Publication History

Abstract

We study a learning-based hierarchical scheduling framework in support of network slicing for cellular networks. This addresses settings where users and/or service classes are grouped into slices, and resources are allocated hierarchically. The hierarchy is implemented by combining a slice-level scheduler which allocates resources to slices, and flow-level schedulers within slices which opportunistically allocate resources to users/services. Optimizing the slice-level scheduler to maximize system utility is typically hard due to underlying heterogeneity and uncertainty in user channels and performance requirements. We address this by reformulating the problem as an online black-box optimization where slice-level schedulers (parameterized by a weight vector) combined with flow-level schedulers result in user/service level stochastic rewards representing performance fitness; the goal is to learn the best weight vector. We develop a bandit algorithm based on queueing cycles by building on Hierarchical Optimistic Optimization (HOO). The algorithm guides the system to improve the choice of the weight vector based on observed rewards. Theoretical analysis of our algorithm shows a sub-linear regret with respect to an omniscient genie. Finally through simulations, we show that the algorithm adaptively learns the optimal weight vectors when combined with opportunistic and/or utility-maximizing flow-level schedulers.

References

[1]
S´ebastien Bubeck, R´emi Munos, Gilles Stoltz, and Csaba Szepesv´ari. X-armed bandits. Journal of Machine Learning Research, 12(5), 2011.
[2]
Jianhan Song, Gustavo de Veciana, and Sanjay Shakkottai. Online learning for hierarchical scheduling to support network slicing in cellular networks. Performance Evaluation, page 102237, 2021.

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  • (2023)DiffPerf: Toward Performance Differentiation and Optimization With SDN ImplementationIEEE Transactions on Network and Service Management10.1109/TNSM.2023.329796621:1(1012-1031)Online publication date: 21-Jul-2023
  1. Online Learning for Hierarchical Scheduling to Support Network Slicing in Cellular Networks

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    Published In

    cover image ACM SIGMETRICS Performance Evaluation Review
    ACM SIGMETRICS Performance Evaluation Review  Volume 49, Issue 3
    December 2021
    77 pages
    ISSN:0163-5999
    DOI:10.1145/3529113
    Issue’s Table of Contents
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 25 March 2022
    Published in SIGMETRICS Volume 49, Issue 3

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    Author Tags

    1. bandit algorithms
    2. network slicing
    3. online learning
    4. scheduling,wireless networks

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    • (2023)DiffPerf: Toward Performance Differentiation and Optimization With SDN ImplementationIEEE Transactions on Network and Service Management10.1109/TNSM.2023.329796621:1(1012-1031)Online publication date: 21-Jul-2023

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