Task Matching and Scheduling in Heterogeneous Systems Using Simulated Evolution

ABSTRACT This paper describes and analyzes the application of a simulated evolution (SE) approach to the problem of matching and scheduling of coarse-grained tasks in a heterogeneous suite of machines. The various steps of the SE algorithm are first discussed. Goodness function required by SE is designed and explained. Then experimental results applied on various types of workloads are analyzed. Workloads are characterized according to the connectivity, heterogeneity, and communication-to-cost ratio of the task graphs. The performance of SE is also compared with a genetic algorithm (GA) approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms

Task Matching and Scheduling in Heterogeneous Systems Using Simulated Evolution

ABSTRACT This paper describes and analyzes the application of a simulated evolution (SE) approach to the problem of matching and scheduling of coarse-grained tasks in a heterogeneous suite of machines. The various steps of the SE algorithm are first discussed. Goodness function required by SE is designed and explained. Then experimental results applied on various types of workloads are analyzed. Workloads are characterized according to the connectivity, heterogeneity, and communication-to-cost ratio of the task graphs. The performance of SE is also compared with a genetic algorithm (GA) approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms

Mapping of subtasks with multiple versions in a heterogeneous ad hoc grid environment

Includes bibliographical references (pages 7-8).An ad hoc grid is a heterogeneous computing system composed of mobile devices. The problem studied here is to statically assign resources to the subtasks of an application, which has an execution time constraint, when the resources are oversubscribed. Each subtask has a preferred version, and a secondary version that uses fewer resources. The goal is to assign resources so that the application meets its execution time constraint while minimizing the number of secondary versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented and 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