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A reinforcement learning approach for the scheduling of live migration from under utilised hosts

Abstract

Live virtual machine migration can have a major impact on how a cloud system performs, as it consumes significant amount of network resources, such as bandwidth. A virtual machine migration occurs when a host becomes over-utilised or under-utilised. In this paper, we propose a network aware live migration strategy that monitors the current demand level of bandwidth when network congestion occurs and performs appropriate actions based on what it is experiencing. The Artificial Intelligence technique that is based on Reinforcement Learning acts as a decision support system, enabling an agent to learn an optimal time to schedule a virtual machine migration depending on the current bandwidth usage in a data centre. We show from our results that an autonomous agent can learn to utilise available network resources such as bandwidth when network saturation occurs at peak times.

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Notes

  1. Environmental Protection Agency (EPA) website link http://www.epa.ie/climate/calculators/.VlWsEnbhBQI.

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Correspondence to Martin Duggan.

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Duggan, M., Duggan, J., Howley, E. et al. A reinforcement learning approach for the scheduling of live migration from under utilised hosts. Memetic Comp. 9, 283–293 (2017). https://doi.org/10.1007/s12293-016-0218-x

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  • DOI: https://doi.org/10.1007/s12293-016-0218-x

Keywords

  • Reinforcement
  • Learning
  • Live
  • Migration
  • Bandwidth