Research Area:  Edge Computing

Abstract:

In recent years, edge computing becomes an increasingly popular alternative. Edge computing allows the computation is implemented in the edge of network, in which the data are stored in the edge of network, to improve the efficiency of data process. However, some resource management techniques in cloud or distributed system cannot better suit for edge computing. Therefore, there exist some challenges on the performance improvement of edge computing. In this paper, the main purpose is to combine the optimal placement of data blocks and the optimal scheduling of tasks to reduce the computation delay and response time for the submitted tasks and improve user experience in edge computing. In optimal placement of data blocks, the value of the data blocks considers not only the popularity of the data blocks, but the data storage capacity and replacement ratios of an edge server that will store those data blocks. Furthermore, the replacement cost for placed data blocks is regarded as an important component of data block placement. This optimal placement scheme can avoid replacing the placed data blocks repeatedly so that the bandwidth overhead is reduced. In optimal scheduling of tasks, the containers are taken as the lightweight resource unit for the services for user requests to make full use of data storage in edge servers and improve the services performance of edge servers. Finally, extensive experiments are conducted to value the performance of task scheduling strategy. The results show that the performance of the proposed task scheduling algorithm is better than that of the compared algorithms.

Keywords:  

Author(s) Name:  ChunlinLi,Jingpan Bai and JianHang Tang

Publisher name:  ELSEVIER

DOI:  10.1016/j.jpdc.2018.11.006

Volume Information:  Volume 125, March 2019, Pages 93-105

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0743731518302661