Abstract
Background & Objective: In today’s scenario, workflow scheduling algorithms require multiple conflicting goals to be optimized. Optimal makespan (schedule length) for the workflow application is the most important criterion to be optimized to achieve desirable performance. Reducing energy consumption for high performance computing requirements is extremely important to control the rapidly growing demand for computation power. This results in a decrease of the operational cost and carbon- dioxide emissions to the environment. Moreover, the computer processors in a heterogeneous environment are not failure free. Any kind of failure can be critical for an application. In this paper, we propose a multi-objective workflow scheduling algorithm in cloud computing – ERAWS, which optimizes three conflicting criterions: makespan, reliability of task execution and energy consumption. We validate and analyze the performance of our algorithm by using the CloudSim toolkit to simulate the cloud environment. We compare the performance of our algorithm with HEFT and ECS, using randomly generated task graphs and task graphs for real world problems like Gaussian Elimination and Fast Fourier Transformation to represent workflow applications.
Conclusion: The simulation results show that the proposed ERAWS algorithm is significantly better than the considered algorithms in terms of makespan, reliability and energy consumption in real world scenarios where reliability and energy consumption are important issues.
Keywords: Cloud computing, reliability, multi objective workflow scheduling, energy-efficiency, Monte Carlo Simulation, green computing.
Graphical Abstract
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