Meta-Heuristic Search Based Model for Task Offloading and Time Allocation in Mobile Edge Computing

Xu, Yufan; Wang, Yan; Yang, Junyao

doi:10.1145/3404555.3404566

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The corresponding resource allocation is derived by defining an offloading priority function and having close to optimal performance in simulations. Similar to [90], Xu et al [111] considered a WPT combined with MEC and proposed a metaheuristic search approach to maximize the weighted sum computation rate of all WD in the network.…”

Section: G) Other Algorithms 1) Heuristic Algorithmsmentioning

confidence: 99%

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Acheampong¹,

Zhang²,

Xu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Task offloading is an important concept for edge computing and the Internet of Things (IoT) because computationintensive tasks must be offloaded to more resource-powerful remote devices. Task offloading has several advantages, including increased battery life, lower latency, and better application performance. A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely. The offloading choice problem is influenced by several factors, including application properties, network conditions, hardware features, and mobility, influencing the offloading system's operational environment. This study provides a thorough examination of current task offloading and resource allocation in edge computing, covering offloading strategies, algorithms, and factors that influence offloading. Full offloading and partial offloading strategies are the two types of offloading strategies. The algorithms for task offloading and resource allocation are then categorized into two parts: machine learning algorithms and non-machine learning algorithms. We examine and elaborate on algorithms like Supervised Learning, Unsupervised Learning, and Reinforcement Learning (RL) under machine learning. Under the non-machine learning algorithm, we elaborate on algorithms like non(convex) optimization, Lyapunov optimization, Game theory, Heuristic Algorithm, Dynamic Voltage Scaling, Gibbs Sampling, and Generalized Benders Decomposition (GBD). Finally, we highlight and discuss some research challenges and issues in edge computing.

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Section: G) Other Algorithms 1) Heuristic Algorithmsmentioning

confidence: 99%

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Acheampong¹,

Zhang²,

Xu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…The distance is to be optimized at which the edge server is to be placed to get the maximum benefits, and more devices have been connected to the server and perform the offloading tasks. Wang et al 13 and Xu et al 14 took the placement of edge server as the optimization problem and assumed that each server has the same computational power to compute and process mobile users and IoT tasks. The objective is to balance the load in such a way that the tasks are divided into equality to not overburden any server at any cost.…”

Section: Related Workmentioning

confidence: 99%

Meta-heuristic-based offloading task optimization in mobile edge computing

Abbas

Raza

Aadil

et al. 2021

International Journal of Distributed Sensor Networks

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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 computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge Computing Environments

Gholipour,

de Assuncao,

Agarwal

et al. 2023

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

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Meta-Heuristic Search Based Model for Task Offloading and Time Allocation in Mobile Edge Computing

Cited by 3 publications

References 17 publications

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

Meta-heuristic-based offloading task optimization in mobile edge computing

TPTO: A Transformer-PPO based Task Offloading Solution for Edge Computing Environments

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