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Adaptive Threshold Based Scheduler for Batch of Independent Jobs for Cloud Computing System
Abstract
Distributed systems are efficient means of realizing high-performance computing (HPC). They are used in meeting the demand of executing large-scale high-performance computational jobs. Scheduling the tasks on such computational resources is one of the prime concerns in the heterogeneous distributed systems. Scheduling jobs on distributed systems are NP-complete in nature. Scheduling requires either heuristic or metaheuristic approach for sub-optimal but acceptable solutions. An adaptive threshold-based scheduler is one such heuristic approach. This work proposes adaptive threshold-based scheduler for batch of independent jobs (ATSBIJ) with the objective of optimizing the makespan of the jobs submitted for execution on cloud computing systems. ATSBIJ exploits the features of interval estimation for calculating the threshold values for generation of efficient schedule of the batch. Simulation studies on CloudSim ensures that the ATSBIJ approach works effectively for real life scenario.
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