Research Article
A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing
@INPROCEEDINGS{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}, proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={EAI}, proceedings_a={QSHINE}, 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
QSHINE
IEEE
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.
