Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

TEMS: An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

Julio Cesar Santos dos Anjos
* ORCID logo ,
João Luiz Grave Gross
,
Kassiano Jose Matteussi
,
Gabriel Villarrubia González
ORCID logo ,
Valderi Reis Quietinho Leithardt
,
Claudio Fernando Resing Geyer
Version 1 : Received: 9 March 2021 / Approved: 10 March 2021 / Online: 10 March 2021 (13:23:33 CET)

A peer-reviewed article of this Preprint also exists.

dos Anjos, J.C.S.; Gross, J.L.G.; Matteussi, K.J.; González, G.V.; Leithardt, V.R.Q.; Geyer, C.F.R. An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture. Sensors 2021, 21, 2914. dos Anjos, J.C.S.; Gross, J.L.G.; Matteussi, K.J.; González, G.V.; Leithardt, V.R.Q.; Geyer, C.F.R. An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture. Sensors 2021, 21, 2914.

Abstract

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of Things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to Cloud Computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in Mobile Edge Computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among Cloud or Mobile Edge Computing (MEC) server or local IoT devices to better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

Keywords

Mobile Edge Computing; Internet Of Things; Cost Minimization Model; Energy Consumption; Scheduling Algorithm

Subject

Computer Science and Mathematics, Algebra and Number Theory

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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