Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems

Ali, Shoukat; Braun, Tracy D.; Siegel, Howard Jay; Maciejewski, Anthony A.; Beck, Noah; Bölöni, Ladislau; Maheswaran, Muthucumaru; Reuther, Albert; Robertson, J; Theys, Mitchell D.; Yao, Bin

doi:10.1016/s0065-2458(04)63003-8

Cited by 45 publications

(46 citation statements)

References 61 publications

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“…ETC matrices were previously used with different degrees of heterogeneity (e.g., [2,6,9,10,31,37]). Most of these ETC matrices were generated by the range-based method described in [9] and the CVB method described in [5].…”

Section: Related Workmentioning

confidence: 99%

“…The assumption of such ETC information is a common practice in resource allocation research (e.g., [6,16,21,26,29,36,39]). An ETC matrix for a given HC system can be obtained from user supplied information, experimental data, or task profiling and analytical benchmarking [2,21,29,39].…”

Section: Introductionmentioning

confidence: 99%

“…The following variables will be used in the calculation of each of the statistical measures: T is the number of tasks to be mapped, M is the number of machines in the system, and μ (t) i is the mean ETC of task i over all machines, given by μ 2 . The standard deviation of the ETC of all tasks on machine j, σ…”

Section: Introductionmentioning

confidence: 99%

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Statistical measures for quantifying task and machine heterogeneities

et al. 2011

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We study heterogeneous computing (HC) systems that consist of a set of different machines that have varying capabilities. These machines are used to execute a set of heterogeneous tasks that vary in their computational complexity. Finding the optimal mapping of tasks to machines in an HC system has been shown to be, in general, an NP-complete problem. Therefore, heuristics have been used to find nearoptimal mappings. The performance of allocation heuristics can be affected significantly by factors such as task and machine heterogeneities. In this paper, we identify different statistical measures used to quantify the heterogeneity of HC systems, and show the correlation between the performance of the heuristics and these measures through simple mapping examples and synthetic data analysis. In addition, we illustrate how regression trees can be used to predict the most appropriate heuristic for an HC system based on its heterogeneity.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical measures for quantifying task and machine heterogeneities

et al. 2011

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“…The estimated time to complete (ETC) values of each task on each machine is assumed to be known based on user-supplied information, experiential data, task profiling and analytical benchmarking, or other techniques (e.g., [1,15,16,21,26,40]). Determination of ETC values is a separate research problem, and the assumption of such ETC information is a common practice in mapping research (e.g., [16,20,21,24,31,39]).…”

Section: Task Modelmentioning

confidence: 99%

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

Kim

Shivle

Siegel

et al. 2007

Journal of Parallel and Distributed Computing

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In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.

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“…The estimated time to compute (ETC) values of each task on each machine are assumed to be known based on user supplied information, experiential data, task profiling and analytical benchmarking, or other techniques (e.g., [1,15,16,19,25,34]). Determination of ETC values is a separate research problem, and the assumption of such ETC information is a common practice in mapping research (e.g., [16,18,19,21,30,33]).…”

Section: Introductionmentioning

confidence: 99%

Dynamic resource allocation heuristics that manage tradeoff between makespan and robustness

et al. 2007

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Heterogeneous parallel and distributed computing systems may operate in an environment where certain system performance features degrade due to unpredictable circumstances. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. This work develops a model for quantifying robustness in a dynamic heterogeneous computing environment where task execution time estimates are known to contain errors. This mathematical expression of robustness is then applied to two different problem environments. Several heuristic solutions to both problem variations are presented that utilize this expression of robustness to influence mapping decisions.

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Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems

Cited by 45 publications

References 61 publications

Statistical measures for quantifying task and machine heterogeneities

Statistical measures for quantifying task and machine heterogeneities

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

Dynamic resource allocation heuristics that manage tradeoff between makespan and robustness

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