Abstract
Computational grids are composed of heterogeneous autonomously managed resources. In such environment, any resource can join or leave the grid at any time. It makes the grid infrastructure unreliable in nature resulting in delay and failure of executing jobs. Thus, fault tolerance becomes a vital aspect of grid for realizing reliability, availability and quality-of-service. The most common technique, for achieving fault tolerance, used in High Performance Computing is rollback recovery. It relies on the availability of checkpoints and stability of storage media. Thus the checkpoints are replicated on storage media. It increases the job execution time, if replication is not done in proper manner. Furthermore, dedicating powerful resources solely as checkpoint storage results in loss of computation power of these resources. It may results in bottlenecks, when the load on the network is high. To address the problem, in this paper checkpoint replication based fault tolerance strategy named as Reliable Checkpoint Storage Strategy (RCSS) is proposed. In RCSS, the checkpoints are replicated on all checkpoint servers in the grid in distributed manner. It decreases the checkpoint replication time and in turn improves the overall job execution time. Additionally, if a resource fails during execution of a job, the RCSS restarts the job from its last valid checkpoint taken from any checkpoint server in the grid. Furthermore to increase the grid performance, CPU cycles of checkpoint servers are also utilized during high load on network. To evaluate the performance of RCSS simulations are carried out using GridSim. The simulation results show that RCSS outperforms in intra-cluster Checkpoint wave completion time by 12.5 % with varying number of checkpoint servers. RCSS also reduces checkpoint wave completion time by 50 % with varying number of clusters. Additionally RCSS reduces replication time within cluster by 39.5 %.