ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (11): 2558-2570.doi: 10.7544/issn1000-1239.2021.20200621

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Resource Deployment with Prediction and Task Scheduling Optimization in Edge Cloud Collaborative Computing

Su Mingfeng1,2, Wang Guojun3, Li Renfa4   

  1. 1(School of Computer Science and Engineering, Central South University, Changsha 410083);2(School of Business Information Technology, Hunan Vocational College of Commerce, Changsha 410205);3(School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006);4(College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082)
  • Online:2021-11-01
  • Supported by: 
    This work was supported by the Key Program of the National Natural Science Foundation of China (61632009), the Natural Science Foundation of Hunan Province (2019JJ70057), the Natural Science Foundation of Guangdong Province (2017A030308006), the National Key Research and Development Program of China (2020YFB1005804), and the Fundamental Research Funds for the Central Universities of Central South University (2018zzts180).

Abstract: The cloud computing model of data centralized processing is facing new challenges for providing diversified application services with rapid interaction and green efficiency. In this paper, the cloud computing capability is extended to the edge devices, and an edge cloud collaborative computing framework is proposed. A resource deployment algorithm based on task prediction (RDTP) is designed. The tasks are predicted by two-dimensional time series in cloud service center, and the task resource deployment of edge server is optimized by classification aggregation and delay threshold determination. A task scheduling algorithm based on Pareto improvement (TSPI) is proposed. At the edge servers, the Pareto progressive comparison is conducted in two stages to obtain the tangent point or any intersection point of the two objective curves of quality of user service and effect of system service to optimize task scheduling. The experimental results show that combining the resource deployment algorithm based on task prediction and the task scheduling algorithm based on Pareto improvement (RDTP-TSPI) increases the average user task hit rate. In addition, in the application scenarios of varying user task scales and different Zipf distribution parameters α, the average service completion time of users, the overall service effectiveness of system, and the total task delay rate of RDTP-TSPI are better than the TSPI and BA (benchmark task scheduling algorithm based on FIFO).

Key words: task scheduling, resource deployment, task prediction, collaborative computing, edge computing

CLC Number: