Robust processor allocation for independent tasks when dollar cost for processors is a constraint

Includes bibliographical references (pages 9-10).In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. Different classes of machines used in such systems typically vary in dollar cost based on their computing efficiencies. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that is optimized. Resource allocation is often done based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. The dollar cost to purchase the machines for use can be a constraint such that only a subset of the machines available can be purchased. The goal of this study is to: (1) select a subset of all the machines available so that the cost constraint for the machines is satisfied, and (2) find a static mapping of tasks so that the robustness of the desired system feature, makespan, is maximized against the errors in task execution time estimates. Six heuristic techniques to this problem are presented and evaluated

Robust static allocation of resources for independent tasks under makespan and dollar cost constraints

Includes bibliographical references (pages 413-414).Heterogeneous computing (HC) systems composed of interconnected machines with varied computational capabilities often operate in environments where there may be inaccuracies in the estimation of task execution times. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that needs to be optimized in such systems. Resource allocation is typically performed based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. In this research, the problem of finding a static mapping of tasks to maximize the robustness of makespan against the errors in task execution time estimates given an overall makespan constraint is studied. Two variations of this basic problem are considered: (1) where there is a given, fixed set of machines, (2) where an HC system is to be constructed from a set of machines within a dollar cost constraint. Six heuristic techniques for each of these variations of the problem are presented and evaluated

Robust Processor Allocation for Independent Tasks When Dollar Cost for Processors is a Constraint

In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. Different classes of machines used in such systems typically vary in dollar cost based on their computing efficiencies. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that is optimized. Resource allocation is often done based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. The dollar cost to purchase the machines for use can be a constraint such that only a subset of the machines available can be purchased. The goal of this study is to: (1) select a subset of all the machines available so that the cost constraint for the machines is satisfied, and (2) find a static mapping of tasks so that the robustness of the desired system feature, makespan, is maximized against the errors in task execution time estimates. Six heuristic techniques to this problem are presented and evaluated

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static allocation of resources for independent tasks under makespa

Processor allocation for tasks that is robust against errors in computation time estimates

Heterogeneous computing systems composed of interconnected machines with varied computational capabilities often operate in environments where there may be sudden machine failures, higher than expected load, or inaccuracies in estimation of system parameters. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that is optimized in such systems. It is important that the makespan of a resource allocation (mapping) be robust against errors in task computation time estimates. The problem of optimally mapping tasks onto machines of a heterogeneous computing environment has been shown, in general, to be NP-complete. Therefore, heuristic techniques to find near optimal solutions to this mapping problem are required. The goal of this research is to find a static mapping of tasks so that the robustness of the desired system feature, makespan, is maximized against the errors in task execution time estimates. Seven heuristics to derive near-optimal solutions and an upper bound to this problem are presented and evaluated