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Neural Computing and Applications

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22-08-2019 | Advances in Parallel and Distributed Computing for Neural Computing

A reformed task scheduling algorithm for heterogeneous distributed systems with energy consumption constraints

Authors: Yikun Hu, Jinghong Li, Ligang He

Published in: Neural Computing and Applications | Issue 10/2020

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Abstract

As the scale increases and performance improves, the energy consumption of high-performance computer systems is rapidly increasing. The energy-aware task scheduling for high-performance computer systems has become a hot spot for major supercomputing centers and data centers. In this paper, we study the task scheduling problem to minimize the schedule length of parallel applications while satisfying the energy constraints in heterogeneous distributed systems. The existing approaches mainly allocate unassigned tasks with minimal energy consumption which cannot achieve optimistic scheduling length in most cases. Based on this situation, we propose a reformed scheduling method with energy consumption constraint algorithm, which is based on an energy consumption level to pre-allocate energy consumption for unassigned tasks. The experimental results show that compared with the existing algorithms, our new algorithm can achieve better scheduling length under the energy consumption constraints.

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Title: A reformed task scheduling algorithm for heterogeneous distributed systems with energy consumption constraints
Authors: Yikun Hu
Jinghong Li
Ligang He
Publication date: 22-08-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04415-2

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