Abstract
In its simplest structure, cloud computing technology is a massive collection of connected servers residing in a datacenter and continuously changing to provide services to users on-demand through a front-end interface. The failure of task during execution is no more an accident but a frequent attribute of scheduling systems in a large-scale distributed environment. Recently, some computational intelligence techniques have been mostly utilized to decipher the problems of scheduling in the cloud environment, but only a few emphasis on the issue of fault tolerance. This research paper puts forward a Checkpointed League Championship Algorithm (CPLCA) scheduling scheme to be used in the cloud computing system. It is a fault-tolerance aware task scheduling mechanisms using the checkpointing strategy in addition to tasks migration against unexpected independent task execution failure. The simulation results show that, the proposed CPLCA scheme produces an improvement of 41%, 33% and 23% as compared with the Ant Colony Optimization (ACO), Genetic Algorithm (GA) and the basic league championship algorithm (LCA) respectively as parametrically measured using the total average makespan of the schemes. Considering the total average response time of the schemes, the CPLCA scheme produces an improvement of 54%, 57% and 30% as compared with ACO, GA and LCA respectively. It also turns out significant failure decrease in jobs execution as measured in terms of failure metrics and performance improvement rate. From the results obtained, CPLCA provides an improvement in both tasks scheduling performance and failure awareness that is more appropriate for scheduling in the cloud computing model. (C) 2017 Elsevier B.V. All rights reserved.