Power savings in software defined data center networks via modified hybrid genetic algorithm

doi:10.1016/S1005-8885(17)60226-7

JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2017, Vol. 24 ›› Issue (4): 76-86.doi: 10.1016/S1005-8885(17)60226-7

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Power savings in software defined data center networks via modified hybrid genetic algorithm

Received:2017-03-13 Revised:2017-06-28 Online:2017-08-30 Published:2017-08-30
Contact: Xiao-Hong Huang E-mail:huangxh@bupt.edu.cn
Supported by:
Joint Funds of National Natural Science Foundation of China and Xinjiang;International Science and Technology Cooperation and Exchange Project of China

Abstract

Abstract: In modern data centers, power consumed by network is an observable portion of the total energy budget and thus improving the energy efficiency of data center networks (DCNs) truly matters. One effective way for this energy efficiency is to make the size of DCNs elastic along with traffic demands by flow consolidation and bandwidth scheduling, i.e., turning off unnecessary network components to reduce the power consumption. Meanwhile, having the instinct support for data center management, software defined networking (SDN) provides a paradigm to elastically control the resources of DCNs. To achieve such power savings, most of the prior efforts just adopt simple greedy heuristic to reduce computational complexity. However, due to the inherent problem of greedy algorithm, a good-enough optimization cannot be always guaranteed. To address this problem, a modified hybrid genetic algorithm (MHGA) is employed to improve the solution’s accuracy, and the fine-grained routing function of SDN is fully leveraged. The simulation results show that more efficient power management can be achieved than the previous studies, by increasing about 5% of network energy savings.

Key words: data center networks, energy efficiency, software defined networking, elastic topology, genetic algorithm

CLC Number:

TP393

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Power savings in software defined data center networks via modified hybrid genetic algorithm

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