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Abstract
Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.
Highlights
The evolution of cloud computing paves the way for realizing the long-held dream of the deliverance of computing resources as a utility to users
The high-level structural design of the ARM algorithm which is executed in the management node is given in Algorithm 1
The proposed task distribution approach for load-balancing among the server facilitates maintaining the maximum number of active servers with the lowest attainable server frequency
Summary
The evolution of cloud computing paves the way for realizing the long-held dream of the deliverance of computing resources as a utility to users. The primary source of energy consumption in a datacenter is its enterprise servers, which consume more than 60%. An idle server consumes approximately two-thirds energy of its 100% utilization at full load [7,8,9]. It is noteworthy that idle power and dynamic power consumption on a utilization level varies based on the different power models of physical servers. The energy reduction caused by shrinking the number of existing resources by virtual machine (VM)-consolidation may spin-off lower-level resource availability resulting in jeopardizing the goodwill and credibility of the provider. The computation capacities of the servers have to be realized and optimum utilization of resources should be achieved [10] to reduce idle and active servers’ power consumption.
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