Online Learning of Weakly Coupled MDP Policies for Load Balancing and Auto Scaling

• URL: http://arxiv.org/abs/2406.14141v1
• Date: Thu, 20 Jun 2024 09:34:24 GMT
• Title: Online Learning of Weakly Coupled MDP Policies for Load Balancing and Auto Scaling
• Authors: S. R. Eshwar, Lucas Lopes Felipe, Alexandre Reiffers-Masson, Daniel Sadoc Menasché, Gugan Thoppe,
• Abstract summary: This paper introduces a novel model and algorithms for tuning load balancers coupled with auto scalers, considering bursty traffic arriving at finite queues. We begin by presenting the problem as a weakly coupled Markov Decision Processes (MDP), solvable via a linear program (LP) We extend it to tackle the problem of online parameter learning and policy optimization using a two-timescale algorithm based on the LP Lagrangian.
• Score: 42.6574685545681
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Load balancing and auto scaling are at the core of scalable, contemporary systems, addressing dynamic resource allocation and service rate adjustments in response to workload changes. This paper introduces a novel model and algorithms for tuning load balancers coupled with auto scalers, considering bursty traffic arriving at finite queues. We begin by presenting the problem as a weakly coupled Markov Decision Processes (MDP), solvable via a linear program (LP). However, as the number of control variables of such LP grows combinatorially, we introduce a more tractable relaxed LP formulation, and extend it to tackle the problem of online parameter learning and policy optimization using a two-timescale algorithm based on the LP Lagrangian.

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