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-make span machines using this iterative approach.