Advertisement
Published in:
2019 | OriginalPaper | Chapter
BTS: Balanced Task Scheduling Strategy Based on Multi-resource Prediction and Allocation in Cloud Environment
Authors : Yongzhong Sun, Kejiang Ye, Wenbo Wang, Cheng-Zhong Xu
Published in: Network and Parallel Computing
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Cloud computing is a new computing paradigm equipped with large-scale servers to satisfy diverse application demands. Managing and scheduling various application tasks on cloud servers is very challenging. In this paper, we propose a Balanced Task Scheduling (BTS) strategy by combining multi-objective particle swarm optimization and time series prediction model to achieve a better load balance among cloud servers. We not only consider the current server load which is used by most existing scheduling methods, but also take the future load change prediction into account. Experiments on the public Alibaba cluster trace with 1310 servers show that the proposed strategy can achieve a more balanced resource utilization.