Efficient SDN-based Task offloading in fog-assisted cloud environment
EAI Endorsed Transactions on Internet of Things, ISSN: 2414-1399, Vol: 10
2024
- 1Citations
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
A distributed computing model called "fog computing" provides cloud-like services which is closer to end devices, and is rapidly gaining popularity. It offers cloud-like computing including storage capabilities, but with less latency and bandwidth requirements, thereby improving the computation capabilities of IoT devices and mobile nodes. In addition, fog computing offers advantages such as support for context awareness, scalability, dependability, and node mobility. Fog computing is frequently used to offload tasks from end devices' applications, enabling quicker execution utilizing the fog nodes' capabilities. Because of the changing nature of the fog environment, task offloading is challenging and the multiple QoS criteria that depend on the type of application being used. This article proposes an SDN-based offloading technique to optimize the task offloading technique for scheduling and processing activities generated by the Internet of Space Things (IoST) devices. The proposed technique utilizes Software-Defined Networking (SDN) optimization to dynamically manage network resources and to facilitate the deployment and execution of offloaded tasks. To model the system which computes the optimal virtual machines (VM) to be allocated in the fog network in order to actively process the offloaded tasks, the GI/G/r queueing model is utilised. This approach minimizes the delay-sensitive task queue and minimises the necessary number of VMs while minimising the waiting time for the fog layer. The findings of the simulation are used to verify the effectiveness of the proposed model.
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