Abstract
With the increasing demand of cloud computing, energy consumption has drawn enormous attention in business and research community. This is also due to the amount of carbon footprints generated from the information and communication technology resources such as server, network and storage. Therefore, the first and foremost goal is to minimize the energy consumption without compromising the customer demands or tasks. On the other hand, task consolidation is a process to minimize the total number of resource usage by improving the utilization of the active resources. Recent studies reported that the tasks are assigned to the virtual machines (VMs) based on their utilization value on VMs without any major concern on the processing time of the tasks. However, task processing time is also equal important criteria. In this paper, we propose a multi-criteria based task consolidation algorithm that assigns the tasks to VMs by considering both processing time of the tasks and the utilization of VMs. We perform rigorous simulations on the proposed algorithm using some randomly generated datasets and compare the results with two recent energy-conscious task consolidation algorithms, namely random and MaxUtil. The proposed algorithm improves about 10 % of energy consumption than the random algorithm and about 5 % than the MaxUtil algorithm.
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Panda, S.K., Jana, P.K. (2016). An Efficient Task Consolidation Algorithm for Cloud Computing Systems. In: Bjørner, N., Prasad, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2016. Lecture Notes in Computer Science(), vol 9581. Springer, Cham. https://doi.org/10.1007/978-3-319-28034-9_8
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DOI: https://doi.org/10.1007/978-3-319-28034-9_8
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