Research Article
A Resource Usage Prediction-Based Energy-Aware Scheduling Algorithm for Instance-Intensive Cloud Workflows
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@INPROCEEDINGS{10.1007/978-3-030-12981-1_44, author={Zhibin Wang and Yiping Wen and Yu Zhang and Jinjun Chen and Buqing Cao}, title={A Resource Usage Prediction-Based Energy-Aware Scheduling Algorithm for Instance-Intensive Cloud Workflows}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={2}, keywords={Energy Instance-intensive Scheduling Cloud workflow Batch processing Prediction}, doi={10.1007/978-3-030-12981-1_44} }- Zhibin Wang
Yiping Wen
Yu Zhang
Jinjun Chen
Buqing Cao
Year: 2019
A Resource Usage Prediction-Based Energy-Aware Scheduling Algorithm for Instance-Intensive Cloud Workflows
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-12981-1_44
Abstract
The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage rediction-based nergy-ware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments.
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