Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence

Cloud computing aims at providing dynamic leasing of server capabilities as scalable, virtualized services to end users. Our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data center. The cloud data center taken into consideration is heterogeneous and large scale in nature. Such a resource pool is basically characterized by high resource dynamics caused by non-linear variation in the availability of processing elements, memory size, storage capacity, bandwidth and power drawn resulting from the sporadic nature of workload. Apart from the said resource dynamics, our proposed work also considers the processor transitions to various sleep states and their corresponding wake up latencies that are inherent in contemporary enterprise servers. The primary objective of the proposed metascheduler is to map efficiently a set of VM instances onto a set of servers from a highly dynamic resource pool by fulfilling resource requirements of maximum number of workloads. As the cloud data centers are overprovisioned to meet the unexpected workload surges, huge power consumption has become one of the major issues of concern. We have proposed a novel metascheduler called Adaptive Power-Aware Virtual Machine Provisioner (APA-VMP) that schedules the workload in such a way that the total incremental power drawn by the server pool is minimum without compromising the performance objectives. The APA-VMP makes use of swarm intelligence methodology to detect and track the changing optimal target servers for VM placement very efficiently. The scenario was experimented by novel Self-adaptive Particle Swarm Optimization (SAPSO) for VM provisioning, which makes best possible use of the power saving states of idle servers and instantaneous workload on the operational servers. It is evident from the results that there is a significant reduction in the power numbers against the existing strategies.

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