Independent Task Scheduling in Heterogeneous System
Sarthak Srivastava1, Santanu Kumar Misra2
1Sarthak Srivastava: pursuing B.Tech, Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim
2Mr. Santanu Kumar Misra department of Computer Science & Engineering, at Sikkim Manipal Institute of Technology, Sikkim.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 10093-10099 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9560118419/2019©BEIESP | DOI: 10.35940/ijrte.D9560.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recently, the rapid development in processing speeds, fast storage devices and better network connectivity, hasaccelerated the popularization of cloud computing. Cloud computing is an on-demand-servicewhich provides users with high end servers,storage and processing capabilities where the user need not be concerned with its infrastructure.Although, there are abundant resources in the cloud infrastructure, for the efficient working and execution of tasks, task scheduling plays a crucial role. Task scheduling results in better performance (throughput) of the system along with better resource utilization which ultimately results inreduced energy consumption. At any given time, a processor should never be in idle state, as it still consumes some amount of energy. In this paper, the use of Quantum Genetic Algorithm has led to the reduction in energy consumption. The objective is to find a scheduling sequencewhich can be implemented ina cloud computing environment. Along with minimizing energy consumption, the algorithm helps reduce makespan time of a processor as well.The results show a decrease in energy consumption by 10-15% under different test scenarios involving a variable number of tasks, processors, and the number of iterations (generations) for which the algorithm was run. The algorithm converges to the desired result within 10-15 iterations, as can be seen from the results published in this paper.
Keywords: Energy Consumption, Makespan, Throughput, Quantum Genetic Algorithm.
Scope of the Article: Energy Harvesting and Transfer for Wireless Sensor Networks.