A Genetic Task Allocation Algorithm for Distributed Computing Systems Incorporating Problem Specific Knowledge

Abstract

Distributed Computing Systems (DCS) promise a convenient platform for parallel processing and consequently can be expected to provide highly improved throughput and turnaround characteristics for all types of computing jobs. Task allocation in DCS remains to be an important and relevant problem attracting the attention of researchers in the discipline. Genetic Algorithms (GA) have successfully been used to solve various optimization problems. A GA based task allocation model for multiprocessors has been proposed by Hou, Ansari & Ren [3]. We present a Genetic Task Allocation Algorithm for DCS, wherein we have considered the underlying interconnection network of the processors, communication requirements among modules of the tasks apart from the precedence relation of the task graph that has been considered in [3] also. We have also considered multiprogramming at every processing nodes with related characteristic values. We have, intentionally, made use of the finding [4] that the incorporation of the problem specific knowledge in construction of GAs improves the initial population structures. The model and algorithm proposed by us is sufficiently simple and adequately usable for the purpose of simulation experiments and its possible incorporation in future operating systems of DCS.