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A Hybrid GRASP-GA based collaborative task offloading technique in fog computing
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DOI：10.1007/s11042-023-15526-3.png)
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The transfer of cloud-computing capabilities at the network's edge reduces the service latency and energy shortage problem in Mobile Fog Computing. Task offloading from resource-constraint devices to the edge servers addresses the limitations of the battery power and computational capability of Mobile Devices (MDs). Most of the offloading strategies proposed earlier comprehended independent tasks. Today's communication network demands the comprehensive study of collaborative task execution. The offloading of latency-sensitive sensor tasks in collaboration at a multi-access edge network promisingly utilizes the edge servers with increased performance. In this paper, we propose a dynamic edge server selection mechanism for task offloading, which collects all the collaborative tasks from allied edge servers and other end devices. A hybrid metaheuristic based on Greedy Randomized Adaptive Search Procedure (GRASP) and Genetic Algorithm (GA) is used to solve the offloading issues. The GRASP -GA based Collaborative Task Offloading technique implemented in the Mobile Fog Computing environment finds the optimal solution. Simulation results show that the proposed study achieves 11% to 63% better performance in depleting the total execution time and energy consumption compared with other existing state-of-art methods.
Keyword:
Collaborative Tasks
Edge Server
Greedy Randomized Adaptive Search Procedure (GRASP)
Mobile Fog Computing
Task Offloading
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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