Workload forecasting based elastic resource management in edge cloud. (January 2020)

• Record Type: Journal Article
• Authors: Liu, Boyun; Guo, Jingjing; Li, Chunlin; Luo, Youlong
• Is Part Of: Computers & industrial engineering. Volume 139(2020)
• Publication Date: 2020-01
• Subjects: Workload forecasting -- Error correction -- Minimizing migration times; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.106136
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: An elastic resource management method based on workload forecasting is studied. A workload forecasting model based on error correction is proposed in this paper. A workload migration model for minimizing migration times is proposed in this paper. The proposed methods can effectively forecast the workload and improve the processing performance of the entire cluster. Abstract: Cloud services are provided at the edge of the network so that data from users can be processed and calculated at the edges. The user's irregular access triggers the fluctuations of the edge cloud workload. Therefore, an elastic resource management method based on workload forecasting in edge clouds is proposed in this paper. When the resource demand is large, more resources are requested from the cloud service provider so that the task can be completed before the deadline. When the resource demand is small, the idle resource is released to meet the cost constraint. The resource demand is judged based on the workload forecasting. In order to improve the accuracy of workload forecasting, a workload forecasting model based on error correction is proposed in this paper. Neither overload nor the light-load status of edge cloud nodes can make full use of the resources. To improve the node processing performance and reduce migration times, a workload migration model for minimizing migration times is proposed in this paper. The experimental results show that the proposed methods can effectively forecast the workload and improve the processing performance of the entire cluster.