Abstract
Virtual machine (VM) technology is one of the energy-efficiency approaches to save energy with acceptable quality of service (QoS). In our previous studies, Artificial Bee Colony (ABC) based VM allocation policy can make a good tradeoff between performance and energy consumption. However, there are two problems in state-of-the-art ABC based approaches: (1) how to find global optimized solutions efficiently; (2) how to minimize the decision time of VM allocation. To solve these two problems, the idea of simulated annealing is adopted to get a better global optimum, and the idea of gradient descent is applied to accelerate the speed of finding solution space inΔt. Compared with state-of-the-art ABC based policies, the experimental results show that the proposed algorithm efficiently reduces energy consumption and SLA violation.
Highlights
At present, a large number of data centers have been put into practice to support internet-based services
This paper presents a new live Virtual machine (VM) allocation policy to reach as far as possible to meet the SLAV value while saving energy consumption
We proposed the state-of-the-art Artificial Bee Colony (ABC) based policy with the idea of simulated annealing and the idea of gradient descent
Summary
A large number of data centers have been put into practice to support internet-based services. Many applications, such as E-commerce and scientific computing, require large-scale data-intensive computing. As data centers and their applications continue increasing, how to realize energyefficiency computing has become a particular challenge. Workload of a data center is a priori unknown to energyefficiency policy and will likely be variable over both time and space. Customers wish their jobs can be finished in time with an acceptable price. Lots of computing and storage nodes are occupied in finishing their jobs in time with high energy consumption and low utilization
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