Virtual machine migration policy for multi-tier application in cloud computing based on Q-learning algorithm

Cloud computing technology provides shared computing which can be accessed over the Internet. When cloud data centers are flooded by end-users, how to efficiently manage virtual machines to balance both economical cost and ensure QoS becomes a mandatory work to service providers. Virtual machine migration feature brings a plenty of benefits to stakeholders such as cost, energy, performance, stability, availability. However, stakeholders’ objectives are usually conflict with each other. Furthermore, the optimal resource allocation problem in cloud infrastructure is usually NP-Hard or NP-Complete class. In this paper, the virtual migration problem is formulated by applying the game theory to ensure both load balance and resource utilization. The virtual machine migration algorithm, named V2PQL, is proposed based on Markov decision process and Q-learning algorithm. The results of the simulation demonstrate the efficiency of our proposal which are divided into training phase and extraction phase. The proposed V2PQL algorithm has been benchmarked to the Round-Robin, inverse Ant System, Max–Min Ant System, and Ant System algorithms in order to highlight its strength and feasibility in extraction phase.