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Dayong W, Bin Abu Bakar K, Isyaku B, Abdalla Elfadil Eisa T, Abdelmaboud A. A comprehensive review on internet of things task offloading in multi-access edge computing. Heliyon 2024; 10:e29916. [PMID: 38698997 PMCID: PMC11064154 DOI: 10.1016/j.heliyon.2024.e29916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/05/2024] Open
Abstract
With the rapid development of Internet of Things (IoT) technology, Terminal Devices (TDs) are more inclined to offload computing tasks to higher-performance computing servers, thereby solving the problems of insufficient computing capacity and rapid battery consumption of TD. The emergence of Multi-access Edge Computing (MEC) technology provides new opportunities for IoT task offloading. It allows TDs to access computing networks through multiple communication technologies and supports more mobility of terminal devices. Review studies on IoT task offloading and MEC have been extensive, but none of them focus on IoT task offloading in MEC. To fill this gap, this paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network. For each paper, the main problems solved by the mechanism, technical classification, evaluation methods, and supported parameters are extracted and analyzed. Furthermore, shortcomings of current research and future research trends are discussed. This review will help potential and new researchers quickly understand the panorama of IoT task offloading approaches in MEC and find appropriate research paths.
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Affiliation(s)
- Wang Dayong
- Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia
| | - Kamalrulnizam Bin Abu Bakar
- Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia
| | - Babangida Isyaku
- Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia
- Department of Computer Science, Faculty of Information Communication Technology, Sule Lamido University, K/Hausa, Jigawa State, Nigeria
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Li W, Sun X, Wan B, Liu H, Fang J, Wen Z. A hybrid GA-PSO strategy for computing task offloading towards MES scenarios. PeerJ Comput Sci 2023; 9:e1273. [PMID: 37346691 PMCID: PMC10280386 DOI: 10.7717/peerj-cs.1273] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/13/2023] [Indexed: 06/23/2023]
Abstract
As a new type of computing paradigm closer to service terminals, mobile edge computing (MEC), can meet the requirements of computing-intensive and delay-sensitive applications. In addition, it can also reduce the burden on mobile terminals by offloading computing. Due to cost issues, results in the deployment density of mobile edge servers (MES) is restricted in real scenario, whereas the suitable MES should be chosen for better performance. Therefore, this article proposes a task offloading strategy under the sparse MES density deployment scenario. Commonly, mobile terminals may reach MES through varied access points (AP) based on multi-hop transmitting mode. The transmission delay and processing delay caused by the selection of AP and MES will affect the performance of MEC. For the purpose of reducing the transmission delay due to system load balancing and superfluous multi-hop, we formulated the multi-objective optimization problem. The optimization goals are the workload balancing of edge servers and the completion delay of all task offloading. We express the formulated system as an undirected and unweighted graph, and we propose a hybrid genetic particle swarm algorithm based on two-dimensional genes (GA-PSO). Simulation results show that the hybrid GA-PSO algorithm does not outperform state-of-the-art GA and NSA algorithms in obtaining all task offloading delays. However, the workload by standard deviation approach is about 90% lower than that of the GA and NSA algorithms, which effectively optimizes the performance of load balancing and verifies the effectiveness of the proposed algorithm.
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Affiliation(s)
- Wenzao Li
- College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China
- Network and Data Security Key Lab. of Sichuan Pro., University of Electronic Science and Technology of China, Chengdu, China
| | - Xiulan Sun
- College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Bing Wan
- School of Software, Chengdu Polytechnic, Chengdu, China
| | - Hantao Liu
- Educational Informationization and Big Data Center, Education Department of Sichuan Province, Chengdu, China
| | - Jie Fang
- College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Zhan Wen
- College of Communication Engineering, Chengdu University of Information Technology, Chengdu, China
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Wu J, Haider SA, Soni M, Kalra A, Deb N. Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing. PeerJ Comput Sci 2022; 8:e1118. [PMID: 36426244 PMCID: PMC9680867 DOI: 10.7717/peerj-cs.1118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.
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Affiliation(s)
- Jianbin Wu
- Computer Science and Engineering Department, Zhejiang Normal University, Jinhua, Zhejiang, China
| | - Sami Ahmed Haider
- Computing Department, University of Worcester, Worcester, United Kingdom
| | - Mukesh Soni
- Changdigarh University, Department of CSE, University Center for Research and Development, Mohali, Punjab, India
| | - Ashima Kalra
- ECE Department, Chandigarh Engineering College, Landran, Mohali, India
| | - Nabamita Deb
- Department of Information Technology, Gauhati University, Assam, India
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Chen X, Gao T, Gao H, Liu B, Chen M, Wang B. A multi-stage heuristic method for service caching and task offloading to improve the cooperation between edge and cloud computing. PeerJ Comput Sci 2022; 8:e1012. [PMID: 35875634 PMCID: PMC9299281 DOI: 10.7717/peerj-cs.1012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
Edge-cloud computing has attracted increasing attention recently due to its efficiency on providing services for not only delay-sensitive applications but also resource-intensive requests, by combining low-latency edge resources and abundant cloud resources. A carefully designed strategy of service caching and task offloading helps to improve the user satisfaction and the resource efficiency. Thus, in this article, we focus on joint service caching and task offloading problem in edge-cloud computing environments, to improve the cooperation between edge and cloud resources. First, we formulated the problem into a mix-integer nonlinear programming, which is proofed as NP-hard. Then, we proposed a three-stage heuristic method for solving the problem in polynomial time. In the first stages, our method tried to make full use of abundant cloud resources by pre-offloading as many tasks as possible to the cloud. Our method aimed at making full use of low-latency edge resources by offloading remaining tasks and caching corresponding services on edge resources. In the last stage, our method focused on improving the performance of tasks offloaded to the cloud, by re-offloading some tasks from cloud resources to edge resources. The performance of our method was evaluated by extensive simulated experiments. The results show that our method has up to 155%, 56.1%, and 155% better performance in user satisfaction, resource efficiency, and processing efficiency, respectively, compared with several classical and state-of-the-art task scheduling methods.
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Affiliation(s)
- Xiaoqian Chen
- Management Center of Informatization, Xinxiang University, Xinxiang, China
| | - Tieliang Gao
- Key Laboratory of Data Analysis and Financial Risk Prediction, Xinxiang University, Xinxiang, China
| | - Hui Gao
- Management Center of Informatization, Xinxiang University, Xinxiang, China
| | - Baoju Liu
- School of Information Engineering, Pingdingshan University, Pingdingshan, China
| | - Ming Chen
- Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Bo Wang
- Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China
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Wang B, Cheng J, Cao J, Wang C, Huang W. Integer particle swarm optimization based task scheduling for device-edge-cloud cooperative computing to improve SLA satisfaction. PeerJ Comput Sci 2022; 8:e893. [PMID: 35494839 PMCID: PMC9044220 DOI: 10.7717/peerj-cs.893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
Task scheduling helps to improve the resource efficiency and the user satisfaction for Device-Edge-Cloud Cooperative Computing (DE3C), by properly mapping requested tasks to hybrid device-edge-cloud resources. In this paper, we focused on the task scheduling problem for optimizing the Service-Level Agreement (SLA) satisfaction and the resource efficiency in DE3C environments. Existing works only focused on one or two of three sub-problems (offloading decision, task assignment and task ordering), leading to a sub-optimal solution. To address this issue, we first formulated the problem as a binary nonlinear programming, and proposed an integer particle swarm optimization method (IPSO) to solve the problem in a reasonable time. With integer coding of task assignment to computing cores, our proposed method exploited IPSO to jointly solve the problems of offloading decision and task assignment, and integrated earliest deadline first scheme into the IPSO to solve the task ordering problem for each core. Extensive experimental results showed that our method achieved upto 953% and 964% better performance than that of several classical and state-of-the-art task scheduling methods in SLA satisfaction and resource efficiency, respectively.
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Affiliation(s)
- Bo Wang
- Zhengzhou University of Light Industry, Zhengzhou, China
| | - Junqiang Cheng
- Europe-Aisa Hi-tech and Digital Technology Company Limited, Zhengzhou, China
| | - Jie Cao
- Zhengzhou University of Light Industry, Zhengzhou, China
| | - Changhai Wang
- Zhengzhou University of Light Industry, Zhengzhou, China
| | - Wanwei Huang
- Zhengzhou University of Light Industry, Zhengzhou, China
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Sang Y, Cheng J, Wang B, Chen M. A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing. PeerJ Comput Sci 2022; 8:e851. [PMID: 35174270 PMCID: PMC8802786 DOI: 10.7717/peerj-cs.851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
Device-edge-cloud cooperative computing is increasingly popular as it can effectively address the problem of the resource scarcity of user devices. It is one of the most challenging issues to improve the resource efficiency by task scheduling in such computing environments. Existing works used limited resources of devices and edge servers in preference, which can lead to not full use of the abundance of cloud resources. This article studies the task scheduling problem to optimize the service level agreement satisfaction in terms of the number of tasks whose hard-deadlines are met for device-edge-cloud cooperative computing. This article first formulates the problem into a binary nonlinear programming, and then proposes a heuristic scheduling method with three stages to solve the problem in polynomial time. The first stage is trying to fully exploit the abundant cloud resources, by pre-scheduling user tasks in the resource priority order of clouds, edge servers, and local devices. In the second stage, the proposed heuristic method reschedules some tasks from edges to devices, to provide more available shared edge resources for other tasks cannot be completed locally, and schedules these tasks to edge servers. At the last stage, our method reschedules as many tasks as possible from clouds to edges or devices, to improve the resource cost. Experiment results show that our method has up to 59% better performance in service level agreement satisfaction without decreasing the resource efficiency, compared with eight of classical methods and state-of-the-art methods.
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Affiliation(s)
- Yongxuan Sang
- Zhengzhou University of Light Industry, Zhengzhou, China
| | - Junqiang Cheng
- Europe-Aisa Hi-tech and Digital Technology Company Limited, Zhengzhou, China
| | - Bo Wang
- Zhengzhou University of Light Industry, Zhengzhou, China
| | - Ming Chen
- Zhengzhou University of Light Industry, Zhengzhou, China
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Raza S, Ayzed Mirza M, Ahmad S, Asif M, Rasheed MB, Ghadi Y. A vehicle to vehicle relay-based task offloading scheme in Vehicular Communication Networks. PeerJ Comput Sci 2021; 7:e486. [PMID: 33954252 PMCID: PMC8053018 DOI: 10.7717/peerj-cs.486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/19/2021] [Indexed: 06/12/2023]
Abstract
Vehicular edge computing (VEC) is a potential field that distributes computational tasks between VEC servers and local vehicular terminals, hence improve vehicular services. At present, vehicles' intelligence and capabilities are rapidly improving, which will likely support many new and exciting applications. The network resources are well-utilized by exploiting neighboring vehicles' available resources while mitigating the VEC server's heavy burden. However, due to the vehicles' mobility, network topology, and the available computing resources change rapidly, which are difficult to predict. To tackle this problem, we investigate the task offloading schemes by utilizing vehicle to vehicle and vehicle to infrastructure communication modes and exploiting the vehicle's under-utilized computation and communication resources, and taking the cost and time consumption into account. We present a promising relay task-offloading scheme in vehicular edge computing (RVEC). According to this scheme, the tasks are offloaded in a vehicle to vehicle relay for computation while being transmitted to VEC servers. Numerical results illustrate that the RVEC scheme substantially enhances the network's overall offloading cost.
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Affiliation(s)
- Salman Raza
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Muhammad Ayzed Mirza
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Shahbaz Ahmad
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - Muhammad Asif
- Department of Computer Science, National Textile University, Faisalabad, Pakistan
| | - Muhammad Babar Rasheed
- Computer Engineering Department, University of Alcalá, Madrid, Spain
- Department of Electronics and Electrical Systems, The University of Lahore, Lahore, Pakistan
| | - Yazeed Ghadi
- Department of Software engineering/Computer Science, Al Ain University of Science and Technology, Abu Dhabi, UAE
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