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

Quintin Fettes, Avinash Karanth, Razvan Bunescu, Brandon Beckwith, Sreenivas Subramoney
4/17/2023
cs.DCcs.LG

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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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Cite this paper

@article{fettes2023reclaimer,
  title  = {Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices},
  author = {Quintin Fettes and Avinash Karanth and Razvan Bunescu and Brandon Beckwith and Sreenivas Subramoney},
  year   = {2023},
  eprint = {2304.07941},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.07941v1}
}

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