Paper Info
Title
A latency-aware and energy-efficient computation offloading in mobile fog computing: a hidden Markov model-based approach
Abstract
In recent years, Fog Computing (FC) is known as a good infrastructure for the Internet of Things (IoT). Using this architecture for the mobile applications in the IoT is named the Mobile Fog Computing (MFC). If we assume that an application includes some modules, thus, these modules can be sent to the Fog or Cloud layer because of the resource limitation or increased runtime at the mobile. This increases the efficiency of the whole system. As data is entered sequentially, and the input is given to the modules, the number of executable modules increases. So, this research is conducted to find the best place in order to run the modules that can be on the mobile, Fog, or Cloud. According to the proposed method, when the modules arrive at gateway, then, a Hidden Markov model Auto-scaling Offloading (HMAO) finds the best destination to execute the module to create a compromise between the energy consumption and execution time of the modules. The evaluation results obtained regarding the parameters of the energy consumption, execution cost, delay, and network resource usage shows that the proposed method on average is better than the local execution, First-Fit (FF), and Q-learning based method.
Year
DOI
Venue
2021
10.1007/s11227-020-03476-8
The Journal of Supercomputing
Keywords
DocType
Volume
Mobile fog computing, Offloading, Hidden Markov model, Energy efficiency, Latency-aware
Journal
77
Issue
ISSN
Citations 
5
0920-8542
1
PageRank 
References 
Authors
0.40
18
3
Name
Order
Citations
PageRank
Fatemeh Jazayeri110.40
Ali Shahidinejad2415.91
Mostafa Ghobaei Arani318916.41