Utility-based resource management in an oversubscribed energy-constrained heterogeneous environment executing parallel applications
Maciejewski, Anthony A.
Siegel, Howard Jay
Koenig, Gregory A.
AffiliationUniv Arizona, Dept Elect & Comp Engn
Resource management heuristics
MetadataShow full item record
PublisherELSEVIER SCIENCE BV
CitationMachovec, D., Khemka, B., Kumbhare, N., Pasricha, S., Maciejewski, A. A., Siegel, H. J., ... & Wright, M. (2017). Utility-based resource management in an oversubscribed energy-constrained heterogeneous environment executing parallel applications. Parallel Computing.
Rights© 2017 Elsevier B.V. All rights reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractThe worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of its completion. The performance of a computing system is measured as the total utility earned by all completed tasks over some interval of time (e.g., 24 h). We have designed, analyzed, and compared the performance of a set of heuristic techniques to maximize system performance when scheduling dynamically arriving parallel tasks onto a high performance computing (HPC) system that is oversubscribed and energy constrained. We consider six utility-aware heuristics and four existing heuristics for comparison. A new concept of temporary place holders is compared with scheduling using permanent reservations. We also present a novel energy filtering technique that constrains the maximum energy-per-resource used by each task. We conducted a simulation study to evaluate the performance of these heuristics and techniques in multiple energy-constrained oversubscribed HPC environments. We conduct an experiment with a subset of the heuristics on a physical testbed system for one scheduling scenario. We demonstrate that our proposed utility-aware resource management heuristics are able to significantly outperform existing techniques. (C) 2017 Elsevier B.V. All rights reserved.
Note24 month embargo; published online: 7 November 2017
VersionFinal accepted manuscript
SponsorsU.S. Department of Energy [DE-AC05-00OR22725]; Department of Energy; Oak Ridge National Laboratory for the Department of Defense (DoD) ; National Science Foundation (NSF) [CCF-1302693, CCF-1547036, CCF-1547035]; NSF [CNS-0923386]