Multilevel heuristics for task assignment in distributed systems

Abstract

Task assignment problem deals with assigning tasks to processors in order to minimize the sum of execution and communication costs in a distributed system. In this work, we propose a novel task clustering scheme which considei s the differences between the execution times of tasks to be clustered as well as the communication costs between them. We use this clustering approach witli proper assignment schemes to implement two-phase assignment algorithms which can be used to find suboptimal solutions to any task assignment problem. In addition, we adapt the multilevel scheme used in graph/hypergrapli partitioning to the task assignment. Multilevel assignment algorithms reduce the size of the original problem by collapsing tasks, find an initial assignment on the smellier problem, and then projects it towards the original problem l)y successively refining the assignment at each level. We propose several clustering schemes for multilevel assignment algorithms. The performance of all proposed algorithms are evaluated through an experimental study where the assignment qualities are compared with two up-to-date heuristics. Experimerita.l results show that our algorithms substantially outperform both of the existing heuristics.