Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices

• URL: http://arxiv.org/abs/2304.07941v1
• Date: Mon, 17 Apr 2023 01:44:05 GMT
• Title: Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices
• Authors: Quintin Fettes, Avinash Karanth, Razvan Bunescu, Brandon Beckwith, Sreenivas Subramoney
• Abstract summary: We introduce Reclaimer, a deep learning model that adapts to changes in the number and behavior of runtime changes in order to minimize CPU core allocation while meeting requirements. When evaluated with two micro-service-based applications, Reclaimer reduces the mean CPU core allocation by 38.4% to 74.4% relative to the industry-standard scaling solution.
• Score: 4.397680391942813
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development, modularity, and isolation. However, dependencies among microservices with uneven execution time may result in longer queues, idle resources, or Quality-of-Service (QoS) violations. In this paper we introduce Reclaimer, a deep reinforcement learning model that adapts to runtime changes in the number and behavior of microservices in order to minimize CPU core allocation while meeting QoS requirements. When evaluated with two benchmark microservice-based applications, Reclaimer reduces the mean CPU core allocation by 38.4% to 74.4% relative to the industry-standard scaling solution, and by 27.5% to 58.1% relative to a current state-of-the art method.

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