Representing Task and Machine Heterogeneities for Heterogeneous Computing Systems


A distributed heterogeneous computing (HC) system consists of diversely capable machines harnessed together to execute a set of tasks that vary in their computational requirements. Heuristics are needed to map (match and schedule) tasks onto machines in an HC system so as to optimize some figure of merit. An HC system model is needed to simulate different HC environments to allow the study of the relative performance of different mapping heuristics under different circumstances. This paper characterizes a simulated HC environment by using the expected execution times of the tasks that arrive in the system on the different machines present in the system. This information is arranged in an "expected time to compute" (ETC) matrix as a model of the given HC system, where the entry (i, j) is the expected execution time of task i on machine j. The ETC model is used to express the heterogeneity among the runtimes of the tasks to be executed, and among the machines in the HC system. An existing range-based technique to express heterogeneity in ETC matrices is described. A coefficient-of-variation based technique to express heterogeneity in ETC matrices is proposed, and compared with the range-based technique. The coefficient-of-variation-based ETC generation method provides a greater control over the spread of values (i.e., heterogeneity) in any given row or column of the ETC matrix than the range-based method.


Electrical and Computer Engineering


United States. Defense Advanced Research Projects Agency


This research was supported by the DARPA/ITO Quorum Program under the NPS subcontract numbers N62271-98-m-0217 and N62271-98-M-0448, and under the GSA subcontract number GS09K99BH0250.

Keywords and Phrases

Cluster Computing; Distributed Computing; Grid Computing; Heterogeneous Computing; Modeling Computer Systems Heterogeneity; Modeling Workload Heterogeneity; Workload Characterization; Computational Methods; Computer Simulation; Heuristic Methods; Information Analysis; Matrix Algebra; Optimization; Systems Analysis; Vectors; Distributed Computer Systems

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Article - Journal

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© 2000 Tamkang University, All rights reserved.

Publication Date

01 Nov 2000

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