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Title	Whale Optimization-Based Task Offloading Technique In Integrated Cloud-Fog Environment
ID_Doc	61636
Authors	Shingare H.; Kumar M.
Year	2023
Published	Lecture Notes in Networks and Systems, 547
DOI	http://dx.doi.org/10.1007/978-981-19-6525-8_35
Abstract	The adoption of Internet of Things (IoT) applications is growing rapidly in various sectors to create a smart atmosphere like automobiles, medicine, smart cities, agriculture, etc. Each IoT device produces enormous data that requires the resources for processing, but these devices have resource constraints. However, the cloud platform executes such types of tasks, but it cannot process the real-time latency-sensitive applications due to communication delay, scalability, and high power consumption. Task offloading becomes a challenging issue, where tasks should be allocated to an efficient resource that can process them without delay and save energy. Hence, fog computing is a promising solution to address the mentioned issue, and it provides a computation-oriented service, is closer to data sources, and improves the QoS metrics including delay and energy. Several task offloading approaches have been proposed to allocate the resource for IoT applications, but are unable to take the appropriate offloading decision and best resource allocation which degrades the QoS parameters. In this paper, a whale optimization approach is proposed that fills up research gaps by taking the offloading decision at run time. The simulation results proved that the proposed approach performs better as compared with the state-of-the-art approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Cloud computing; Fog computing; Internet of Things; Task offloading; Whale Optimization(WOA)

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