Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centers

In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA), Optimised Firefly Search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC

Energy optimization methods for Virtual Machine Placement in Cloud Data Center

The Information Technology industry has been upheaved by the influx of cloud computing. The extension of Cloud computing has resulted in the creation of huge data centers globally containing numbers of computers that consume large amounts of energy resulting in high operating costs. To reduce energy consumption providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an efficient way. The problems of VM consolidation are host overload detection, host under-load detection, VM selection and VM placement. Each of the aforestated sub-problems must operate in an optimized manner to maintain the energy usage and performance. The process of VM placement has been focused in this work, and energy efficient, optimal virtual machine placement (E2OVMP) algorithm has been proposed. This minimizes the expenses for hosting virtual machines in a cloud provider environment in two different plans such as i) reservation and ii) on-demand plans, under future demand and price uncertainty. It also reduces energy consumption. E2OVMP algorithm makes a decision based on the gilt-edged solution of stochastic integer programming to lease resources from cloud IaaS providers. The performance of E2OVMP is evaluated by using CloudSim with inputs of planet lab workload. It minimized the user’s budget, number of VM migration resulting efficient energy consumption. It ensures a high level of constancy to the Service Level Agreements (SLA).Keywords: Cloud resource management; virtualization; dynamic consolidation; stochastic integer programming (SIP)*Cite as: Esha Barlaskar, N. Ajith Singh, Y. Jayanta Singh, “Energy optimization methods for Virtual Machine Placementin Cloud Data Center†ADBU J.Engg.Tech., 1(2014) 0011401(7pp

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