A Fault Tolerant Adaptive Method for the Scheduling of Tasks in Dynamic Grids

An essential issue in distributed high-performance computing is how to allocate efficiently the workload among the processors. This is specially important in a computational Grid where its resources are heterogeneous and dynamic. Algorithms like Quadratic Self-Scheduling (QSS) and Exponential Self-Scheduling (ESS) are useful to obtain a good load balance, reducing the communication overhead. Here, it is proposed a fault tolerant adaptive approach to schedule tasks in dynamic Grid environments. The aim of this approach is to optimize the list of chunks that QSS and ESS generates, that is, the way to schedule the tasks. For that, when the environment changes, new optimal QSS and ESS parameters are obtained to schedule the remaining tasks in an optimal way, maintaining a good load balance. Moreover, failed tasks are rescheduled. The results show that the adaptive approach obtains a good performance of both QSS and ESS even in a highly dynamic environment.