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

Autor:	Cláudio F. R. Geyer, Valderi Reis Quietinho Leithardt, João Luiz Grave Gross, Kassiano J. Matteussi, Gabriel Villarrubia González, Julio C. S. dos Anjos
Rok vydání:	2021
Předmět:	Internet of things Computer science Cloud computing TP1-1185 02 engineering and technology Biochemistry Article Analytical Chemistry Scheduling (computing) Task (project management) cost minimization model energy consumption Server 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Instrumentation Mobile edge computing business.industry Chemical technology 020208 electrical & electronic engineering Context (computing) scheduling algorithm 020206 networking & telecommunications Energy consumption Atomic and Molecular Physics and Optics The Internet mobile edge computing business Algorithm
Zdroj:	Sensors Volume 21 Issue 9 Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 2914, p 2914 (2021)
ISSN:	1424-8220
DOI:	10.3390/s21092914
Popis:	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 achieve 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%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fb1db12df993727061391ab4aa69281 https://doi.org/10.3390/s21092914 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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