2021
DOI: 10.1177/15501477211023021
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Meta-heuristic-based offloading task optimization in mobile edge computing

Abstract: With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high … Show more

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Cited by 16 publications
(6 citation statements)
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“…It is not greedy in particular. In particular, a slight regression of a method can also be tolerated the designed to simulate methodology which helps to investigate an optimal solution very systematically and therefore to achieve a better solution that can often correspond with either the best solution [ 6 , 39 , 40 ]. Metaheuristic would be a problem-independent method, but to apply the methodology to the nature of the problem, it will be essential to do great of all its intrinsic parameters.…”
Section: System Design Of Offloadingmentioning
confidence: 99%
See 1 more Smart Citation
“…It is not greedy in particular. In particular, a slight regression of a method can also be tolerated the designed to simulate methodology which helps to investigate an optimal solution very systematically and therefore to achieve a better solution that can often correspond with either the best solution [ 6 , 39 , 40 ]. Metaheuristic would be a problem-independent method, but to apply the methodology to the nature of the problem, it will be essential to do great of all its intrinsic parameters.…”
Section: System Design Of Offloadingmentioning
confidence: 99%
“…To improve the computational difficulties of smart devices with offloading, cloud computing is considered to play a significant role [ 3 5 ]. By optimizing the device's parameters so that it becomes easier to find optimal decisions for offloading tasks, a metaheuristic algorithm is used to migrate the data or to offload the task [ 6 ]. When comparing the default algorithm FCFS to ACO or PSO, we find that PSO has a lower battery or makespan time than FCFS or ACO [ 7 ].…”
Section: Introductionmentioning
confidence: 99%
“…The studies to date have focused on optimizing latency and energy consumption during computational offloading. Abbas et al utilized meta-heuristic algorithms, such as ant colony, whale, and grey wolf optimization, to establish an offloading strategy aimed at minimizing energy and delay, albeit within the confines of a singular MEC server context [16]. While meta-heuristic algorithms are robust, their complexity and numerous parameters often complicate the tuning process.…”
Section: Related Workmentioning
confidence: 99%
“…On the basis of these methods, a security mechanism was introduced in IoT networks for the healthcare applications in different works. These studies implemented closely related existing schemes in the simulation, for instance, delay optimal long short-term memory (LSTM) [ 21 ], workflow metaheuristic system (WFMS), [ 22 ] and workflow metaheuristic cloud (WMC) [ 23 ]. These studies are closely related to our work to execute workflow applications on heterogeneous nodes in cloud computing.…”
Section: Related Workmentioning
confidence: 99%