Study of an iterative resource allocation technique to minimize machine completion times in a distributed computing system

Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to maximize the combined performance and/or cost effectiveness. Heuristics for allocating resources in an HC system have different optimization criteria. A common optimization criterion is to minimize the completion time of the last to finish machine (makespan). In some environments, it is useful to minimize the finishing times of the other machines in the system, i.e., those machines that are not the last to finish. Consider a production environment where a set of known tasks are to be mapped to resources off-line before execution begins. In this study, we present an "iterative" approach for decreasing the finishing time of each machine in a given resource allocation, by repeatedly running a mapping heuristic to minimize makespan on all machines and then the non-makespan machines; i.e., ignoring the current makespan machine and the tasks assigned to it. This work identifies heuristics that can offer improvements in the completion time of non-makespan machines using this "iterative" approach

Robust resource allocation in weather data processing systems

Includes bibliographical references (pages [9-10]).Reliability of weather data processing systems is of prime importance to ensure the efficient operation of space-based weather monitoring systems. This work defines a heterogeneous weather data processing system that is susceptible to uncertainties in data set arrival times. The resource allocation must be robust with respect to these uncertainties. The tasks to be executed by the data processing system are classified into three broad categories: telemetry, tracking and control (high priority); data processing (medium priority); and data research (low priority).The high priority tasks must be completed before considering medium and low priority tasks. The goal of this research is to find a resource allocation that minimizes makespan of the high priority tasks, and to find a mapping that maximizes a function of the completion time and priority of the medium and low priority tasks. Different heuristic techniques to find near optimal solutions are studied, and their performance is evaluated

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

Study of an Iterative Technique to Minimize Completion Times of Non-Makespan Machines

Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces to maximize the combined performance and/or cost effectiveness of the system. Heuristics for allocating resources in an HC system have different optimization criteria. A common optimization criterion is to minimize the completion time of the last to finish machine (makespan). In some environments, it is useful to minimize the finishing times of the other machines in the system, i.e., those machines that are not the last to finish. Consider a production environment where a set of known tasks are to be mapped to resources off-line before execution begins. Minimizing the finishing times of all the machines will provide the earliest available ready time for these machines to execute tasks that were not initially considered. In this study, we examine an iterative approach that decreases machine finishing times by repeatedly running a resource allocation heuristic. The goal of this study is to investigate whether this iterative procedure can reduce the finishing time of some machines compared to the mapping initially generated by the heuristic. We show that the effectiveness of the iterative approach is heuristic dependent and study the behavior of the iterative approach for each of the chosen heuristics. This work which identifies heuristics can and cannot attain improvements in the completion time of non-makespan machines using this iterative approach

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static allocation of resources for independent tasks under makespa

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

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

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