Research Area:  Cloud Computing

Abstract:

Optimizing energy consumption in cloud computing is yet a challenge despite the diversity of the proposed energy management strategies. Indeed, and during our related work study we have observed that the different elements or components which should be considered in order to be able to properly manage energy consumption in a cloud computing context are not well defined and/or discussed in terms of importance. This makes the proper classification and/or comparison of the different proposed strategies or techniques very difficult. Consequently, this paper aims, on the one hand, at defining and discussing properly such components in order to create a guideline and, on the other hand, to ease both the classification and the comparison of these proposed strategies and techniques. Second and after discussing some common weaknesses related to the current energy consumption optimization techniques and methods, this paper proposes energy-saving technique which uses a novel load detecting policy. This policy is based on the median absolute deviation method which uses the median and the standard deviation to calculate upper and lower thresholds which aim to classify hosts into either overloaded or under-loaded state. Simulation results have shown better results of the proposed technique compared to the existing ones especially in reducing energy consumption and the number of virtual machine migrations in addition to better active host time. Indeed, we found that the average of saved energy is around 40% compared to the built in techniques in cloudSim.

Keywords:  
Energy management strategy
Load detecting policy
VM selection policy
VM placement policy
Cloud computing

Author(s) Name:   Taha Chaabouni & Maher Khemakhem

Publisher name:  Springer

DOI:  10.1007/s11227-017-2154-z

Volume Information:  volume 74, pages 6569–6597 (2018)

Paper Link:   https://link.springer.com/article/10.1007/s11227-017-2154-z