Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories
Computer Networks, ISSN: 1389-1286, Vol: 234, Page: 109915
2023
- 9Citations
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- 1Mentions
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New Findings from Harbin Engineering University in the Area of Engineering Reported (Emergency Task Offloading Strategy Based On Cloud-edge-end Collaboration for Smart Factories)
2023 OCT 02 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- New research on Engineering is the subject of a
Article Description
The realization of smart factories is becoming increasingly possible due to the rapid development of edge computing technology and 5G communication technology. Smart devices in smart factories can perform tasks such as environmental monitoring, production scheduling, and autonomous mobility. These tasks cannot be completed on time depending only on the computing power of smart devices. Therefore, some tasks need to be performed on edge servers and cloud servers. However, events such as natural disasters, network attacks, and hardware failures will cause server computing resource failures. These threats bring hidden dangers to the normal operation of smart factories. This paper proposes an emergency offloading strategy based on cloud–edge-end collaboration for smart factories. In this strategy, the total task execution delay and the key task execution delay constitute an objective function, which is solved by a Fast Chemical Reaction Optimization (Fast-CRO) algorithm. The algorithm follows the principle of prioritizing the offloading of key tasks in emergency situations, and it quickly makes emergency offloading decisions for the system. Simulation results show that this strategy outperforms the other five baseline algorithms and existing priority based CRO algorithms.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1389128623003602; http://dx.doi.org/10.1016/j.comnet.2023.109915; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85166484976&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1389128623003602; https://dx.doi.org/10.1016/j.comnet.2023.109915
Elsevier BV
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