Abstract
With a significant increase in the compute-intensive mobile applications in recent times, the seamless integration of the cloud platform with mobile devices becomes essential. As a result, the mobile cloud computing (MCC) and mobile edge computing (MEC) paradigm become prominent in today’s era. At the same time, scheduling user applications in these domains have also gained research attention. But a majority of scheduling policies for the MEC environment consider offloading and execution of tasks only between two computing platforms: (i) mobile and edge or (ii) edge and cloud. In this paper, we consider the scheduling of a set of user applications with partial offloading and execution of tasks in all the three layers of compute stack: the mobile device, edge, and the cloud, to minimize the energy consumption of the edge nodes. Our extensive simulation reveals that the proposed scheduling strategy achieves an energy reduction up to 30% with an execution speedup up to 6% while meeting the delay constraints of the applications.
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Deka, V., Ghose, M., Nandi, S. (2022). Energy-aware Application Scheduling on DVFS-Enabled Edge Computing with Mobile–Edge–Cloud Cooperation. In: Patgiri, R., Bandyopadhyay, S., Borah, M.D., Emilia Balas, V. (eds) Edge Analytics. Lecture Notes in Electrical Engineering, vol 869. Springer, Singapore. https://doi.org/10.1007/978-981-19-0019-8_43
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DOI: https://doi.org/10.1007/978-981-19-0019-8_43
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