A Markov-Based Prediction Model for Host Load Detection in Live VM Migration

Host load detection algorithm determines if a given host is overload or underloaded then the decision can be made to migrate VMs to achieve host/server consolidation and load balancing in cloud data centers while satisfying the QoS constraints. Presently, host load detection is a challenging problem in the cloud data center management specially with high dynamic environment for the host load. In this paper, we propose a novel Markov-based prediction algorithm to forecast the future load state of the host. The experimental results demonstrate that the proposed algorithm has better performance than the other competitive algorithms. The results for different types of PlanetLab real and random workloads show significant reduction of the SLA violation and the number of VM migrations.