Open AccessEfficient energy and completion time for dependent task computation offloading algorithm in industry 4.0
Author(s) -
Rabab Farouk Abdel-Kader,
Noha Emad El-Sayad,
Rawya Rizk
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0252756
Subject(s) - computer science , cloud computing , algorithm , energy consumption , efficient energy use , task (project management) , quality of service , computation offloading , firefly algorithm , distributed computing , real time computing , computer network , edge computing , particle swarm optimization , engineering , operating system , systems engineering , electrical engineering
Rapid technological development has revolutionized the industrial sector. Internet of Things (IoT) started to appear in many fields, such as health care and smart cities. A few years later, IoT was supported by industry, leading to what is called Industry 4.0. In this paper, a cloud-assisted fog-networking architecture is implemented in an IoT environment with a three-layer network. An efficient energy and completion time for dependent task computation offloading (ET-DTCO) algorithm is proposed, and it considers two quality-of-service (QoS) parameters: efficient energy and completion time offloading for dependent tasks in Industry 4.0. The proposed solution employs the Firefly algorithm to optimize the process of the selection-offloading computing mode and determine the optimal solution for performing tasks locally or offloaded to a fog or cloud considering the task dependency. Moreover, the proposed algorithm is compared with existing techniques. Simulation results proved that the proposed ET-DTCO algorithm outperforms other offloading algorithms in minimizing energy consumption and completion time while enhancing the overall efficiency of the system.