Research Article
A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing
@ARTICLE{10.4108/eai.19-8-2015.2260146, author={Liyun Zuo and Lei Shu and Shoubin Dong and Zhangbing Zhou and Lei Wang}, title={A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={1}, number={2}, publisher={EAI}, journal_a={CS}, year={2015}, month={9}, keywords={cloud computing, energy, load evaluation}, doi={10.4108/eai.19-8-2015.2260146} }- Liyun Zuo
Lei Shu
Shoubin Dong
Zhangbing Zhou
Lei Wang
Year: 2015
A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing
CS
EAI
DOI: 10.4108/eai.19-8-2015.2260146
Abstract
Cloud resource and its load have dynamic characteristics. To address this challenge, a dynamic self-adaptive evaluation method (termed SDWM) is proposed in this paper. SDWM uses some dynamic evaluation indicators to evaluate resource state more accurately. And it divides the resource load into three states -- $Overload$, $Normal$ and $Idle$ by the self-adaptive threshold. Then it migrates overload resources to balance load, and releases idle resources whose idle times exceed a threshold to save energy, which can effectively improve system utilization. Experimental results demonstrate SDWM has better adaptability than other similar methods when resources dynamically join or exit. This shows the positive effect of the dynamic self-adaptive threshold.
Copyright © 2015 L. Shu al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
