Task assignment in distributed computing systems

TLDR: A technique based on the problem-space genetic algorithm (PSGA) for the static task assignment problem in homogeneous distributed computing systems is introduced to reduce the task turnaround time and to increase the throughput of the system by properly balancing the load and reducing the interprocessor communication time among processors.

Abstract: We introduce a technique based on the problem-space genetic algorithm (PSGA) for the static task assignment problem in homogeneous distributed computing systems to reduce the task turnaround time and to increase the throughput of the system by properly balancing the load and reducing the interprocessor communication time among processors. The PSGA based approach combines the power of genetic algorithms, a global search method, with simple and fast problem-specific heuristic to search a large solution space efficiently and effectively to find the best possible solution in an acceptable cpu time. The proposed scheme is applied to a digital signal processing (DSP) system consisting of 119 tasks to illustrate its balancing properties and computational advantage on a large system. The proposed scheme offers 12%-30% improvement in the assignment cost as compared to the previous best known results for the DSP example. >