Adaptive energy-efficient scheduling algorithm for parallel tasks on homogeneous clusters

Increasing attention has been directed towards the two key issues of performance and energy consumption for parallel applications in high performance clusters. The traditional energy-efficient scheduling algorithms mainly leverage a threshold to balance system performance and energy consumption. But...

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Veröffentlicht in:Journal of network and computer applications Jg. 41; S. 101 - 113
Hauptverfasser: Liu, Wei, Du, Wei, Chen, Jing, Wang, Wei, Zeng, GuoSun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.05.2014
Elsevier
Schlagworte:
Adaptive threshold
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Dynamic voltage scaling (DVS)
Electrical engineering. Electrical power engineering
Energy efficiency
Exact sciences and technology
Homogeneous clusters
Language processing and microprogramming
Power electronics, power supplies
Software
Task duplication
Task duplication
Energy efficiency
Adaptive threshold
Homogeneous clusters
Dynamic voltage scaling (DVS)
Performance evaluation
Energy consumption
Adaptive algorithm
Adaptive method
Performance requirement
Energetic efficiency
Threshold
High performance
Parallel algorithm
Probabilistic approach
Duplication
Cluster
Power electronics
Scheduling
Distributed system
Energy savings
Scaling law
Experimental result
Parallelism
Energy management
ISSN:1084-8045, 1095-8592
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Zusammenfassung:Increasing attention has been directed towards the two key issues of performance and energy consumption for parallel applications in high performance clusters. The traditional energy-efficient scheduling algorithms mainly leverage a threshold to balance system performance and energy consumption. But the random threshold cannot flexibly adapt the system characters and application requirements, thus making the scheduling results instable. In this paper, we propose a novel two-phase Adaptive Energy-efficient Scheduling (AES), which combines the Dynamic Voltage Scaling (DVS) technique with the adaptive task duplication strategy. The AES algorithm justifies threshold automatically, thus improving the system flexibility. In the first phase, we propose an adaptive threshold-based task duplication strategy, which can obtain an optimal threshold. It then leverages the optimal threshold to balance schedule lengths and energy savings by selectively replicating predecessor of a task. Therefore, the proposed task duplication strategy can get the suboptimal task groups that not only meet the performance requirement but also optimize the energy efficiency. In the second phase, it schedules the groups on DVS-enabled processors to reduce processor energy whenever tasks have slack time due to task dependencies. To illustrate the effectiveness of AES, we compare it with the duplication-based algorithms and the DVS-based algorithms. Extensive experimental results using the real-world applications demonstrate that our algorithm can effectively save energy while maintaining a good performance.
ISSN:1084-8045
1095-8592
DOI:10.1016/j.jnca.2013.10.009