A reliable-adaptive scheduler for computational grids with failure recovery and rescheduling mechanisms

Computational grids (CGs) have become an attractive research area as they suggest a suitable environment for developing large scale parallel applications. CGs integrate a large amount of distributed heterogeneous resources into a single powerful platform. However, to make good use of CGs, grid resources should be scheduled efficiently. Various scheduling strategies have been introduced, including static and dynamic behaviours. The former maps tasks to resources at submission time, while the latter operates at schedule time. While static scheduling is unsuitable for the dynamic grid environment, scheduling in CGs is still more complex than the proposed dynamic ones. This paper introduces a decentralised adaptive grid scheduler (AGS) based on a novel rescheduling mechanism. AGS has several salient properties as it is: hybrid, adaptive, decentralised, and efficient. AGS is also robust as it has the ability to: 1) detect resource failures; 2) continue its functionality in spite of the failure existence; 3) recover back as soon as possible. Moreover, it integrates both static and dynamic scheduling behaviours. An initial static scheduling map is proposed for an input directed acyclic graph (DAG). However, DAG tasks may be rescheduled if the hosting resources' performance changes in a way that affects the tasks' response time. AGS tries to overcome drawbacks of traditional schedulers by utilising the mobile agent unique features to enhance the resource discovery and monitoring processes. Experimental results have shown that AGS outperforms traditional grid schedulers as it introduces a better scheduling efficiency.