An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems

• Published in: PloS one Vol. 12; no. 5; p. e0177567
• Main Authors: Idris, Hajara, Ezugwu, Absalom E, Junaidu, Sahalu B, Adewumi, Aderemi O
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.05.2017; Public Library of Science (PLoS)
• Subjects: Ad hoc networks; Algorithms; Analysis; Animals; Ant colony optimization; Ants - physiology; Avoidance; Biology and Life Sciences; Business machines; Checkpointing; Cloud computing; Clustering; Colleges & universities; Colonies; Complexity; Computational grids; Computer and Information Sciences; Computer applications; Computer engineering; Computer networks; Computer science; Computer simulation; Computers; Computing time; Concurrency; Crashes; Distributed processing; Electrical engineering; Engineering; Engineering and Technology; Environment; Environments; Failure; Fault tolerance; Gene mapping; Genetic algorithms; Geographical distribution; Grid computing; Immunological tolerance; Institutions; Integration; International conferences; Job hunting; Mathematical Computing; Mathematics; Migration; Mobile communication systems; Models, Theoretical; Networking; Optimization; Physical Sciences; Recovery; Reliability; Research and Analysis Methods; Resource management; Scheduling; Servers; Simulation; Social Sciences; Statistics; Wireless networks
• DOI: 10.1371/journal.pone.0177567
• ISSN: 1932-6203

The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.