Dynamic Offloading Framework in Fog Computing

Dynamic Offloading Framework in Fog Computing
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Jyoti Yadav, Suman Sangwan
DOI : 10.14445/22315381/IJETT-V70I7P204

How to Cite?

Jyoti Yadav, Suman Sangwan, "Dynamic Offloading Framework in Fog Computing" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 32-42, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P204

Abstract
To meet the needs of Internet of Things (IoT) devices, fog computing has emerged as a new paradigm. Offloading computation tasks is one of the most crucial issues in a fog environment. Computation offloading is the process by which devices send computation-intensive tasks to servers for processing. Because of network constraints, not all computation tasks can be delegated to servers. As a result, it is critical to determine how many tasks should be run on servers and how many should be run locally. Furthermore, due to the uncertainty in the requirements, finding the server to execute the offloaded task in a vibrant environment is difficult. To address this issue, a dynamic computation offloading framework has been proposed. Here clustering is used to locate the decision engine, and fuzzy logic is used for offloading decisions. The major objective of the paper is to determine whether to offload or not depending on CPU usage, delay sensitivity, residual energy, and task size and bandwidth. Our algorithm makes dynamic decisions by sending time-sensitive tasks to local devices or fog nodes for processing and resource-intensive tasks to the cloud. According to simulation results, the proposed algorithm is more efficient than the Energy-aware offloading clustering approach (EAOCA) and the Fuzzy-based offloading algorithm regarding task successful execution, CPU utilization, and average delay. It improves the rate of successfully executed tasks by 5.92% over EAOCA and 4.72% over Fuzzy based approach. It reduces delay by 11.73% over EAOCA and 8.74 % over the Fuzzy approach.

Keywords
Fog computing, Clustering, Offloading, Delay, Fuzzy logic.

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