Abstract
Cloud data centers consume substantial amount of energy to meet up the growing demand of cloud resources. Recently, increase in enormous data processing and computing in data centers has resulted rigorous energy consumption that leads to large amount of carbon footprint and creates the harmful impact on the environment. The two main components of a data center that are contributing to energy consumption: computing energy and cooling energy. In this paper, we have considered the problem of scheduling parallel applications consisting of dependent tasks having precedence constraints in heterogeneous cloud computing environment. Recently many heuristics have been developed to optimize the execution time, i.e., makespan only without giving much deliberation to the computing energy and cooling energy needed for cooling the data center while performing the tasks. Reducing energy consumption leads to reduction in operating costs. Therefore, we proposed an algorithm namely power and temperature-aware workflow-scheduling (PATA-WSD) algorithm with user-specified SLA constraint (deadline) to optimize computing as well as cooling energy. The proposed algorithm minimizes the energy consumption (computing + cooling) of executing tasks along with satisfying deadline constraints with the use of dynamic voltage and frequency scaling technique. The simulation results are obtained using random task graphs and real-time scientific workflows such as Cybershake and Montage. Results demonstrated that the proposed algorithm optimizes the computing and cooling energy consumption.
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Southern California Earthquake Center. http://www.scec.org
Montage: An Astronomical Image Engine. http://montage.ipac.caltech.edu
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Rani, R., Garg, R. Power and Temperature-Aware Workflow Scheduling Considering Deadline Constraint in Cloud. Arab J Sci Eng 45, 10775–10791 (2020). https://doi.org/10.1007/s13369-020-04879-8
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DOI: https://doi.org/10.1007/s13369-020-04879-8