Open AccessFog computing task scheduling optimization based on multi-objective simplified swarm optimization
Author(s) -
Wei-Chang Yeh,
Chyh-Ming Lai,
Kuan-Cheng Tseng
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1411/1/012007
Subject(s) - cloud computing , computer science , fog computing , scheduling (production processes) , distributed computing , computation , latency (audio) , task (project management) , swarm behaviour , real time computing , mathematical optimization , operating system , artificial intelligence , algorithm , engineering , telecommunications , mathematics , systems engineering
With the dramatic growth of data volume, the cloud computing structure has faced a severe difficulty, the latency. To deal with this problem, researchers have proposed the fog computing structure, which can successfully release the computation loads from one datacenter of cloud to multiple local fog devices. Hence, the tasks will be processed at the local fog device and avoid transmitting to datacenter which is not cost-effective, and the results can be transmitted to the users immediately. The main differences of task scheduling between cloud computing and fog computing are the processors specifications, such as processing rate, cost per unit time. The processing rate of processors in datacenter is fast but the cost per unit time is costly. However, the processing rate of fog devices is not as fast as datacenter but it is relatively cost-effective. Consequently, we are facing a multi-objective optimization problem. Hence, we adopted an elite MOSSO (Multi-Objective Simplified Swarm Optimization) with considering the characteristics of fog computing paradigm.