A network aware approach for the scheduling of virtual machine migration during peak loads

作者：Duggan Martin^*; Duggan Jim; Howley Enda; Barrett Enda

来源：Cluster Computing, 2017, 20(3): 2083-2094.

DOI：10.1007/s10586-017-0948-7

• Live migration
• Bandwidth
• Reinforcement learning
• Network flow
• Peak load

Live virtual machine migration can have a major impact on how a cloud system performs, as it consumes significant amounts of network resources such as bandwidth. Migration contributes to an increase in consumption of network resources which leads to longer migration times and ultimately has a detrimental effect on the performance of a cloud computing system. Most industrial approaches use ad-hoc manual policies to migrate virtual machines. In this paper, we propose an autonomous network aware live migration strategy that observes the current demand level of a network and performs appropriate actions based on what it is experiencing. The Artificial Intelligence technique known as Reinforcement Learning acts as a decision support system, enabling an agent to learn optimal scheduling times for live migration while analysing current network traffic demand. We demonstrate that an autonomous agent can learn to utilise available resources when peak loads saturate the cloud network.

• 出版日期2017-9