Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices
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Abstract (arXiv)
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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References · 8
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
- 2020DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inferencecited by 54
- 2020FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservicescited by 41
- 2018Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learningcited by 4
- 2018Seer: Leveraging Big Data to Navigate the Increasing Complexity of Cloud Debuggingcited by 1
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