Abstract
The rapid growth of IoT devices and applications with data-intensive processing has led to energy consumption and latency concerns. These applications tend to offload task processing to remote Data Centers in the Cloud, distant from end-users, increasing communication latency and energy costs. In such a context, this work proposes a dynamic cost model to minimize energy consumption and total elapsed time for IoT devices in Mobile Edge Computing environments. The solution presents a Time and Energy Minimization Scheduler (TEMS) that executes the cost model, validated through simulation, which resulted in a reduction in energy consumption by up to 51.61% and in task completion time by up to 86.65%.
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Notes
- 1.
MEC Simulator available at https://github.com/jlggross/MEC-simulator.
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Acknowledgment
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. PROPESQ-UFRGS and by projects: “GREEN-CLOUD: Computação em Cloud com Computação Sustentável” (#162551-0000 488-9) and SmartSent (#172551-0001 195-3) from FAPERGS, PNPD Capes, and CNPq Brazil, program PRONEX 122014.
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Gross, J.L.G., Matteussi, K.J., dos Anjos, J.C.S., Geyer, C.F.R. (2020). A Dynamic Cost Model to Minimize Energy Consumption and Processing Time for IoT Tasks in a Mobile Edge Computing Environment. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_8
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