Resource management of cloud-enabled systems using model-free reinforcement learning


The digital system of the future will face the growing challenge of controlling the system behavior in complex dynamically evolving environments. In this paper, we examine the applicability of a new management paradigm based on a reinforcement learning approach, where no preliminary specification of the system model is required. The learning agent identifies the most adequate control policies in live interaction with a partially observed system and provides it with autonomous management capabilities. We present the results of experimentation with cloud-based applications and discuss the technical challenges that need to be addressed in this field. Furthermore, we present the results of experimentation on a 5G network slice that hosts a cloud-based application in a multi-agent reinforcement learning setting, and demonstrate the value of information exchange between learning agents.

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Correspondence to Yue Jin.

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Jin, Y., Bouzid, M., Kostadinov, D. et al. Resource management of cloud-enabled systems using model-free reinforcement learning. Ann. Telecommun. 74, 625–636 (2019).

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  • Reinforcement learning
  • Cloud computing
  • Application scaling
  • Resource optimization