Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Liao, Zhuofan; Peng, Jingsheng; Xu, Bing; Huang, Jiawei

doi:10.1186/s13677-021-00232-y

Cited by 49 publications

(20 citation statements)

References 30 publications

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“…Even though the bandwidth capacity of a single set of network equipment has now developed to more than THz, it cannot meet the huge number of concurrent tasks. For example, part of the time, no matter what kind of Internet access is available, it will become abnormally slow, which is deeply felt by people [5]. (1) A huge number of concurrent computing task requests, even if the individual task data volume is not large, will also bring the server overwhelmed; (2) the communication link of the service request is too long, often having to go through multiple routers forwarding, to reach the target server; (3) the operator of the router arrangement, not following the maximum demand to build, of course, cannot carry the burst of computing task requests [6].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Machine Learning‐Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

2021

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This paper presents an in-depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-integer nonlinear optimization model for energy consumption minimization is developed. Then, based on the analysis of the concave-convex properties of this optimization model, this paper uses variable relaxation and nonconvex optimization theory to transform the problem into a convex optimization problem. Subsequently, two optimization algorithms are designed: for the relaxation optimization problem, an iterative optimization algorithm based on the Lagrange dual method is designed; based on the branch-and-bound integer programming method, the iterative optimization algorithm is used as the basic algorithm for each step of the operation, and a global optimization algorithm is designed for transmitting power allocation, computational offloading strategy, dynamic adjustment of local computing power, and receiving energy channel selection strategy. Finally, the simulation results verify that the scheduling strategy of the frequency division technique proposed in this paper has good energy consumption minimization performance in mobile edge computation offloading. Our model is highly efficient and has a high degree of accuracy. The anomaly detection method based on a decision tree combined with deep learning proposed in this paper, unlike traditional IoT attack detection methods, overcomes the drawbacks of rule-based security detection methods and enables them to adapt to both established and unknown hostile environments. Experimental results show that the attack detection system based on the model achieves good detection results in the detection of multiple attacks.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Machine Learning‐Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

2021

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“…The combination of edge computing and the next generation of cellular networks and computational networks from end-end devices can be offloaded accurately and on time by edge servers on base stations (BSs). To increase the communication and computation quantity of IoT systems, multiaccess edge computing (MEC) has recently developed the capable technology to solve this problem [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

Hasanin

Alsobhi

Khadidos

et al. 2021

Applied Bionics and Biomechanics

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Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensive tasks to the edge servers to save energy and shorten their latency. The bald eagle algorithm (BES) is the advanced optimization algorithm that resembles the strategy of eagle hunting. The strategies are select, search, and swooping stages. Previously, the BES algorithm is used to consume the energy and distance; to improve the better energy and reduce the offloading latency in this research and some delays occur when devices increase causes demand for cloud data, it can be improved by offering ROS (resource) estimation. To enhance the BES algorithm that introduces the ROS estimation stage to select the better ROSs, an edge system, which offloads the most appropriate IoT subtasks to edge servers then the expected time of execution, got minimized. Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. The latency is reduced from the cloud to the local; this can be overcome by using edge computing, and deep learning expects faster and better results from the network. This can be proposed by BES algorithm technique that is better than other conventional methods that are compared on results to minimize the offloading latency. Then, the simulation is done to show the efficiency and stability by reducing the offloading latency.

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“…Gu et al [29,30] proposed a secure framework for data queries in fog and cloud environments. Liao et al proposed [31,32,33] task offloading algorithms in edge computing, providing cost savings related to data transmission, latency, and bandwidth usage, among other benefits.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

Poonam¹,

Sangwan²

2023

Computers, Materials &Amp; Continua

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This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed. It balances the load on cloud and cloudlet while minimizing makespan and execution time. However, it may trap in local optimum; levy flight can handle it. Hybridization of fuzzy firefly with levy flight is a novel technique that provides reduced makespan, execution time, and Degree of imbalance while balancing the load. Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration (NASA) and Clarknet datasets. Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker (ACOQDM), Distributed Scheduling Optimization Algorithm (DSOA), and Utility-based Firefly Algorithm (UFA) when compared in terms of makespan, Degree of imbalance, and Figure of Merit.

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Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Cited by 49 publications

References 30 publications

[Retracted] Machine Learning‐Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

[Retracted] Machine Learning‐Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

Fuzzy Firefly Based Intelligent Algorithm for Load Balancing in Mobile Cloud Computing

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