Run-time adaptation for grid environments

In this paper, we study a general mapping problem where a set of independent tasks compete for the shared resources of a Grid environment. Tasks have resource co-allocationrequirements. Each task requires multiple and different resources to be allocated simultaneously. At run-time, a task may release its allocated resources during its execution and before its completion time. Our objective is to minimize the overall schedule length of all submitted tasks while satisfying all resource sharing constraints among them. We develop a two-phase mapping approach for solving this problem. The first phase of our approach is off-line planning phase where a schedule plan, which gives a scheduling order and resource assignments of tasks, is generated at compile-time. The second phase is run-time adaptationphase. The goal of the second phase is to improve the performance of the schedule plan by adapting to run-time changes such as the early release of resources and the variationin computation and communication costs. Adaptation may involve changing the scheduling order and resource assignments of the original schedule plan. Our experimental results demonstrate the effectiveness of our approach compared to a baseline algorithm that performs no adaptation at run-time and to a dynamic algorithm that performs no planning at compile-time. Our two-phase mapping approach outperforms both algorithms by up to 20% with respect to the overall schedule length.