Multi-armed Bandits for Self-distributing Stateful Services across Networking Infrastructures

被引:0
作者
Rappa, Frederico Meletti [1 ]
Rodrigues-Filho, Roberto [2 ]
Panisson, Alison R. [2 ]
Marcolino, Leandro Soriano [3 ]
Bittencourt, Luiz F. [1 ]
机构
[1] Univ Estadual Campinas, Inst Comp, Campinas, SP, Brazil
[2] Univ Fed Santa Catarina, Dept Comp, Ararangua, SC, Brazil
[3] Univ Lancaster, Sch Comp & Commun, Lancaster, England
来源
PROCEEDINGS OF 2024 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM, NOMS 2024 | 2024年
基金
巴西圣保罗研究基金会;
关键词
stateful service mobility; edge-cloud infrastructures; reinforcement learning; self-distributing systems;
D O I
10.1109/NOMS59830.2024.10575692
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The investigation of stateful service mobility across networking infrastructures is becoming increasingly important as applications require stateful services capable of migrating from centralized cloud data centers to edge computing infrastructures. State-of-the-art approaches propose either machine learning solutions for stateless service placement or stateful service mobility using static and inflexible state management strategies. We believe these approaches fall short of addressing the full length of the stateful service mobility problem. In this paper, we revisit an emerging concept named self-distributing systems, where a local executing application manages to detach some of its constituent (often stateful) components and place them in remote machines as a solution for stateful service mobility. In previous work, a machine learning approach to support self-distributing systems has not been thoroughly investigated. We model the distribution of stateful components across networking infrastructures as a multi-armed bandits problem and use the UCB1 algorithm to solve it as a first attempt at a flexible solution for stateful service mobility. We conclude the paper by discussing the main challenges and opportunities in this area.
引用
收藏
页数:6
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