BTS: Balanced Task Scheduling Strategy Based on Multi-resource Prediction and Allocation in Cloud Environment - Network and Parallel Computing Access content directly
Conference Papers Year : 2019

BTS: Balanced Task Scheduling Strategy Based on Multi-resource Prediction and Allocation in Cloud Environment

Yongzhong Sun
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  • PersonId : 1161683
Kejiang Ye
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Wenbo Wang
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  • PersonId : 1161684
Cheng-Zhong Xu
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  • PersonId : 1161685

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.
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hal-03770558 , version 1 (06-09-2022)

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Yongzhong Sun, Kejiang Ye, Wenbo Wang, Cheng-Zhong Xu. BTS: Balanced Task Scheduling Strategy Based on Multi-resource Prediction and Allocation in Cloud Environment. 16th IFIP International Conference on Network and Parallel Computing (NPC), Aug 2019, Hohhot, China. pp.345-349, ⟨10.1007/978-3-030-30709-7_31⟩. ⟨hal-03770558⟩
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