A dynamic cluster job scheduling optimisation algorithm based on data irreversibility in sensor cloud. (20th September 2019)

• Record Type: Journal Article
• Authors: Sun, Zeyu; Liu, Jun; Xing, Xiaofei; Li, Chuanfeng; Pan, Xiaoyan
• Is Part Of: International journal of embedded systems. Volume 11:Number 5(2019)
• Page Start: 551
• Page End: 561
• Publication Date: 2019-09-20
• Subjects: sensor cloud -- cluster rendering -- job scheduling -- data irreversibility; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

The optimisation algorithm based on irreversible data for the job scheduling of dynamic cluster is crucial to the improvement of the cluster rendering throughput and the cluster rendering efficiency. However, the dispatching imbalance of the cluster rendering task on the massive number of cluster rendering nodes will prolong the waiting time for the completion of job. Therefore, we propose dynamic cluster job scheduling optimisation algorithm based on data irreversibility (DCJS_DI). Firstly, we analyse the job scheduling target. Then, we exploit the frame independence with the clustering rendering and further establish the job scheduling model for the irreversible data dynamic cluster. Next, we elaborate on the job scheduling process of the irreversible data dynamic cluster and the dispatching process of the cluster rendering task. Finally, we investigate via simulation results the impacts of the job hunger and the resource fragmentation issue of the traditional job scheduling strategies on the system performance, the impacts of the multi-progress and multi-threading cluster rendering on the job completion time and the resource efficiency of the irreversible data dynamic cluster. We further study the extension of the cluster computation capability and the reliability issue of the cloud service.