Energy-Efficient Statistical Live Virtual Machine Placement for Big Data Information Systems in Cloud Computing Environments

With the increasing applications of big data computing on large scale cloud platforms, virtual machines (VM) are utilized to provide flexibility and availability for big data information systems. Energy efficient VM management and distribution on cloud platforms has become an important research subject. Mapping VMs "correctly" into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It must also take into accounts VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. However, the current VM placement approaches are mostly built in a server cluster with homogeneous nodes. Moreover, those approaches are not effective for live migrations with multiple considerations at the same time. In this paper, we propose an energy efficient statistical live VM placement scheme in a heterogeneous server clusters. Our scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all the VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption, and it is ready to be extended with more considerations on users demand. We conduct extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of our scheme.