Application of Polling Scheduling in Mobile Edge Computing

Xiong, Wei; Yang, Zhijun; Ding, Hongwei

doi:10.3390/axioms12070709

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…However, MDBO still faces challenges in obtaining the theoretical optimum when solving some complex problems in a short time. In future work, on the one hand, some other novel algorithms can be combined to improve the efficiency and optimization ability of the algorithm; on the other hand, the optimized algorithm can be used to solve more complex optimization problems in reality, such as the UAV path planning, polling system [45,46], and the NP-hard problem.…”

Section: Discussionmentioning

confidence: 99%

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Ye,

Zhou,

Yang

et al. 2024

Biomimetics

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The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed “Mean Differential Variation”, to enhance the algorithm’s ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.

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Section: Discussionmentioning

confidence: 99%

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Ye,

Zhou,

Yang

et al. 2024

Biomimetics

Self Cite

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“…It is determined by the following Equation (28): applications. SCDBO is not only applicable to these engineering problems, but also to solving optimization problems in edge computing and resource scheduling [24,25].…”

Section: Unmanned Aerial Vehicle Path Planningmentioning

confidence: 99%

A Sinh–Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems

Wang,

Wei,

Guo

et al. 2024

Biomimetics

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The Dung beetle optimization (DBO) algorithm, devised by Jiankai Xue in 2022, is known for its strong optimization capabilities and fast convergence. However, it does have certain limitations, including insufficiently random population initialization, slow search speed, and inadequate global search capabilities. Drawing inspiration from the mathematical properties of the Sinh and Cosh functions, we proposed a new metaheuristic algorithm, Sinh–Cosh Dung Beetle Optimization (SCDBO). By leveraging the Sinh and Cosh functions to disrupt the initial distribution of DBO and balance the development of rollerball dung beetles, SCDBO enhances the search efficiency and global exploration capabilities of DBO through nonlinear enhancements. These improvements collectively enhance the performance of the dung beetle optimization algorithm, making it more adept at solving complex real-world problems. To evaluate the performance of the SCDBO algorithm, we compared it with seven typical algorithms using the CEC2017 test functions. Additionally, by successfully applying it to three engineering problems, robot arm design, pressure vessel problem, and unmanned aerial vehicle (UAV) path planning, we further demonstrate the superiority of the SCDBO algorithm.

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Analysis and prediction of UAV-assisted mobile edge computing systems

Wang,

Yang,

Ding

et al. 2023

MBE

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<abstract><p>As the demand for the internet of things (IoT) continues to grow, there is an increasing need for low-latency networks. Mobile edge computing (MEC) provides a solution to reduce latency by offloading computational tasks to edge servers. However, this study primarily focuses on the integration of back propagation (BP) neural networks into the realm of MEC, aiming to address intricate network challenges. Our innovation lies in the fusion of BP neural networks with MEC, particularly for optimizing task scheduling and processing. Firstly, we introduce a drone-assisted MEC model that categorizes computation offloading into synchronous and asynchronous modes based on task scheduling. Secondly, we employ Markov chains and probability-generation functions to accurately compute parameters such as average queue length, cycle time, throughput, and average delay in the synchronous mode. We also derive the first and second-order derivatives of the probability-generation function to support these computations. Finally, we establish a BP neural network to solve for the average queue length and latency in the asynchronous mode. Our results from the BP neural network closely align with the theoretical values obtained through the probability-generation function, demonstrating the effectiveness of our approach. Additionally, our proposed UAV-assisted MEC model outperforms the synchronous mode. Overall, our MEC scheduling approach significantly reduces latency, enhances speed, and improves throughput, with our model reducing latency by approximately 11.72$ \% $ and queue length by around 9.45$ \% $.</p></abstract>

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Application of Polling Scheduling in Mobile Edge Computing

Cited by 3 publications

References 44 publications

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

A Sinh–Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems

Analysis and prediction of UAV-assisted mobile edge computing systems

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