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Performance-driven scheduling for malleable workloads

Almaaitah, Njoud O. ; Singh, David E. ; Özden, Taylan ; Carretero, Jesus (2024)
Performance-driven scheduling for malleable workloads.
In: Journal of Supercomputing, 2024
doi: 10.1007/s11227-023-05882-0
Article, Bibliographie

Abstract

The development of adaptive scheduling algorithms that take advantage of malleability has become a crucial area of research in many large-scale projects. Malleable workloads can improve the system’s performance but, at the same time, provide an extra dimension to the scheduling problem. This paper proposes an adaptive, performance-based job scheduling method that emphasizes the backfilling concept with malleability. The proposed method performs the malleability operations only when the estimated execution time of the involved applications is better than or equal to the execution time on the allocated resources without reconfiguration. The reconfiguration feasibility is determined by performance models considering the application scalability and reconfiguration overheads. Different policies for implementing malleability are presented, each targeting a specific workload in terms of job size and scalability. The comprehensive evaluation shows an improvement in the slowdown up to 49% compared to the non-adaptive baseline scheduling algorithm.

Item Type: Article
Erschienen: 2024
Creators: Almaaitah, Njoud O. ; Singh, David E. ; Özden, Taylan ; Carretero, Jesus
Type of entry: Bibliographie
Title: Performance-driven scheduling for malleable workloads
Language: English
Date: 29 January 2024
Publisher: Springer
Journal or Publication Title: Journal of Supercomputing
Volume of the journal: 2024
DOI: 10.1007/s11227-023-05882-0
Abstract:

The development of adaptive scheduling algorithms that take advantage of malleability has become a crucial area of research in many large-scale projects. Malleable workloads can improve the system’s performance but, at the same time, provide an extra dimension to the scheduling problem. This paper proposes an adaptive, performance-based job scheduling method that emphasizes the backfilling concept with malleability. The proposed method performs the malleability operations only when the estimated execution time of the involved applications is better than or equal to the execution time on the allocated resources without reconfiguration. The reconfiguration feasibility is determined by performance models considering the application scalability and reconfiguration overheads. Different policies for implementing malleability are presented, each targeting a specific workload in terms of job size and scalability. The comprehensive evaluation shows an improvement in the slowdown up to 49% compared to the non-adaptive baseline scheduling algorithm.

Uncontrolled Keywords: EU/BMBF|ADMIRE
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Parallel Programming
Date Deposited: 07 Mar 2024 13:30
Last Modified: 04 Jun 2024 12:27
PPN: 518818152
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