QGLG Automatic Energy Gear-Shifting Mechanism with Flexible QoS Constraint in Cyber-Physical Systems: Designing, Analysis, and Evaluation

QGLG Automatic Energy Gear-Shifting Mechanism with Flexible QoS Constraint in Cyber-Physical Systems: Designing, Analysis, and Evaluation

Xindong You (Beijing Institute of Graphic Communication, Tsinghua University, Beijing, China), Yeli Li (Beijing Institute of Graphic Communication, Tsinghua University, Beijing, China), Zhenyang Zhu (Hangzhou Dianzi University, Hangzhou, China), Lifeng Yu (Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China) and Dawei Sun (School of Information Engineering, China University of Geosciences, Beijing, China)
Copyright: © 2018 |Pages: 23
DOI: 10.4018/JDM.2018010103

Abstract

This article describes how with the continuous expansion on the volume of data produced by sensors in Cyber Physical Systems, the scale of the cloud storage system has become larger. This will lead to the problems of a high energy consumption rate and a low utilization becoming a serious issue. In order to enhance the effective energy consumption, reduce the invalid energy consumption, and supply more flexible QoS for users in CPS, this article proposes an automatic energy gear-shifting mechanism with flexible QoS constraints (QGLG). The QGLG predicts system load of the follow-up period through a support vector machine model. According to the current system load, the predicted load, and the flexible QoS, QGLG automatically up-shifts and down-shifts among nodes. Substantive results from the simulation experiments done on GridSim show that the QGLG can achieve energy consumption reduction while satisfying the user's flexible QoS requirements. Compared with a similar energy-reducing mechanism, QGLG has its obvious advantage when considering the requirements of user with energy saved notwithstanding.
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Cyber Physical Systems are increasingly deployed over the cloud in a wide range of applications, including smart grids, sensing, computation, storage and other operations, all of them generate a large number heterogeneous data with varying volume, velocity, variety, and value. Near recently, there are some publication discuss the research issues in Cyber-Physical Systems (Choi et al., 2017). Users need to communicate with the cloud to conduct transactions, such as selling and buying energy via a backbone architecture. Therefore, Energy management is a grand research challenges in Cyber Physical Cloud Systems, and has a direct impact on environment. How to ensure the energy is consumed intelligently in Cloud Storage Systems will be main concern both in academia and industry field.

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