Task allocation and migration on a star-network

Modern day applications require computational power which cannot be satisfied with uniprocessor systems. So the use of multiprocessor systems in such jobs becomes necessary. This thesis presents an approach of allocating the tasks to a multiprocessor system called the star network. Generally, an incoming task requires only a part of the star network, and not the whole network, for its execution. So, we need a task allocation strategy which can identify the free processors forming a substar and allocate tasks to these substars. The task executes for a time equal to task residence time and then relinquishes the substar. Sometimes there might be enough free processors forming a substar in the network which can host the next incoming task. But the allocation strategy may not recognize the free processors as a substar. To create a substar of free processors to host the next task, task migration has to be performed such that the free processors are grouped into a substar. In this work, three processor allocation strategies: static, dynamic and dynamic work task migration are presented. Using simulations, a comparison of these strategies is done to obtain the percentage improvement of one strategy over the other. Also a comparative study of the working of these strategies in star-networks and hypercubes is done. A saving of 5-11% is achieved by for both the networks incorporating task-migration in dynamic allocation over simple dynamic allocation

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum