Survey on Multiple Methods for Task Offloading in Mobile Cloud-Edge Environment
College of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
| Abstract: | Mobile edge computing (MEC) is an emerging technology that extends cloud computing to the edge of the network. It offloads computing intensive tasks to the edge server to solve the problem of insufficient computing power and resource of the terminal device, meets the low energy consumption and low latency requirements of the mobile user for application task computing, and greatly releases the pressure of the cloud center. Cloud-edge integration architecture has become a trend. How to perform optimal offload scheduling for terminal tasks has always been one of the key issue in the field of MEC research. This paper reviews the research directions and achievements of task offloading technology in today’s cloud-edge environment. First, the development status of MEC environment and task offloading technology are introduced, and the concepts and applications of classic schemes such as heuristic algorithm, metaheuristic algorithm, and reinforcement learning are elaborated. Based on the mainstream solutions in the research literature, two types of task offloading solutions are summarized: traditional task offloading solutions based on algorithm optimization and interactive task offloading solutions based on reinforcement learning. Second, the research literature on the two mainstream directions mentioned above is summarized and organized from aspects such as problem constraints, optimization objectives, and contributions made. Meanwhile, schemes are compared through the classic algorithms and innovations they used. Finally, the current challenges and future research objectives in this field are summarized to provide reference and help for the follow-up research. |
| Keywords: | Mobile Edge Computing; Task Offloading; Combinatorial Optimization; Reinforcement Learning; Heuristic Algorithm |
| DOI: | 10.57237/j.cst.2023.03.001 |
View:
Downloads:
Received: 9 April 2023
Accepted: 25 May 2023
Published Online: 15 June 2023
Funding
| 1. | 湖南省自然科学基金 (2023JJ50204) |
References
| [1] | Ejaz A, Mubashir H. Mobile Edge Computing: Opportunities, solutions, and challenges [J]. Future Generation Computer Systems, 2017, 70: 59-63. |
| [2] | Mach P, Becvar Z. Mobile edge computing: a survey on architecture and computation offloading [J]. IEEE Communications Surveys & Tutorials, 2017, 19 (3): 1628-1656. |
| [3] | Abbas N, Zhang Y, Taherkordi A, et al. Mobile edge computing: A survey [J]. IEEE Internet of Things Journal, 2017, 5 (1): 450-465. |
| [4] | 李强, 杜婷婷, 童钊, 张锦, 王胜春. 移动边缘计算中基于深度强化学习的依赖任务卸载研究 [J]. 小型微型计算机系统, 2022, 1-8. |
| [5] | Tong Z, Ye F, Mei J, et al. A novel task offloading algorithm based on an integrated trust mechanism in mobile edge computing [J]. Journal of Parallel and Distributed Computing, 2022, 169: 185-198. |
| [6] | 宁振宇, 张锋巍, 施魏松. 基于边缘计算的可信执行环境研究 [J]. 计算机研究与发展, 2019, 56 (7): 1441-1453. |
| [7] | 施魏松, 张星洲, 王一帆, 张庆阳. 边缘计算：现状与展望[J]. 计算机研究与发展, 2019, 56 (1): 69-89. |
| [8] | Jiang C, Cheng X, Gao H, et al. Towards computation offloading in edge computing: A survey [J]. IEEE Access, 2019, 7: 131543-131558. |
| [9] | Wei P, Guo K, Li Y, et al. Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence [J]. IEEE Access, 2022, 10: 65156-65192. |
| [10] | Shakarami A, Ghobaei-Arani M, Shahidinejad A. A survey on the computation offloading approaches in mobile edge computing: a machine learning based perspective [J]. Computer Networks, 2020, 182: 107496. |
| [11] | ETSI, Mobile-edge computing introductory technical white paper. White Paper. Mobile-Edge Computing Industry Initiative, 2014, 1089-7801. |
| [12] | Pallis G, Vakali A. Insight and perspectives for content delivery networks [J]. Communications of the ACM. 2006. 49 (1): 101-106. |
| [13] | Satyanarayanan M, Bahl V, Caceres R, et al. The case for VM-based cloudlets in mobile computing [J]. IEEE Pervasive Computing. 2009, 8 (4): 14-23. |
| [14] | Wang T, Liang Y, Tian Y, et al. Solving coupling security problem for sustainable sensor-cloud systems based on fog computing [J]. IEEE Transactions on Sustainable Computing, 2021, 6 (1): 43-53. |
| [15] | 周悦芝, 张迪. 近端云计算: 后云计算时代的机遇与挑战 [J]. 计算机学报, 2019, 42 (4): 677-700. |
| [16] | Zhang Y, Liang Y, Yin M, et al. Survey on the Methods of Computation Offloading in Mobile Edge Computing [J]. Chinese Journal of Computers, 2021, 44 (12): 2406-2430. |
| [17] | Mao Y, You C, Zhang J, et al. A Survey on Mobile Edge Computing: The Communication Perspective [C]. IEEE Communications Surveys & Tutorials, 2017: 2322-2358. |
| [18] | Wong V, Schober R, Ng D, et al. Key Technologies for 5G Wireless Systems [D]. Cambridge: Cambridge University Press, 2017: 1-2. |
| [19] | Zhang Y, Liu H, Jiao L, et al. To offload or not to offload: An efficient code partition algorithm for mobile cloud computing. [C] // Proceedings of the 2012 IEEE 1st International Conference on Cloud Networking. Paris, French, 2012: 80-86. |
| [20] | 赵志为, 闵革勇. 边缘计算: 原理、技术与实践 [M]. 北京:机械工业出版社, 2021: 103-104. |
| [21] | 温文波, 杜维. 蚁群算法概述 [J]. 石油化工自动化, 2002 (1): 19-22. |
| [22] | Topcuoglu H, Hariri S, Wu M. Performance-effective and low-complexity task scheduling for heterogeneous computing [J]. IEEE Transactions on Parallel and Distributed Systems, 2002, 13 (3): 260-274. |
| [23] | Sagnika s, Bilgaiyan s, Mishra, et al. Workflow scheduling in cloud computing environment using bat algorithm [C] // In proceedings of First International Conference on Smart System, Innovations and Computing, Singapore, 2018: 149-163. |
| [24] | Alireza Askarzadeh. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm [J]. Computers & Structures, 2016, 169: 1-12. |
| [25] | Beegom A, Rajasree M. Integer-PSO: a discrete PSO algorithm for task scheduling in cloud computing system [J]. Evolutionary Intelligence, 2019, 12 (2): 227-239. |
| [26] | 吉根林. 遗传算法研究综述 [J]. 计算机应用与软件, 2004, 21 (2): 69-73. |
| [27] | Zhang L, Wang L, Xiao M, et al. EM_WOA: A budget-constrained energy consumption optimization approach for workflow scheduling in clouds [J]. Peer-to-Peer Networking and Applications, 2022, 15: 973-987. |
| [28] | Richard S. Sutton, Andrew G. Barto. 强化学习 [M]. 第二版. 俞凯, 等译. 北京: 电子工业出版社, 2019: 10-11. |
| [29] | Tong Z, Deng X, Mei J, et al. Response time and energy consumption co-offloading with SLRTA algorithm in cloud–edge collaborative computing [J]. Future Generation Computer Systems, 2022, 129: 64-76. |
| [30] | Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning [J]. Nature, 2015, 518 (7540): 529-533. |
| [31] | Keqin Li. Design and Analysis of Heuristic Algorithms for Energy-Constrained Task Scheduling With Device- Edge-Cloud Fusion [J]. IEEE Transactions on Sustainable Computing. 2022, 1-13. |
| [32] | Keqin Li. Non-clairvoyant and randomised online task offloading in mobile edge computing [J]. International Journal of Parallel, Emergent and Distributed Systems, 2022, 37: 413-424. |
| [33] | Cheng S, Huang J, Chen Z, et al. Approximated Assignment Algorithms for Unordered and Ordered Tasks in Data Shared MEC Systems [J]. IEEE Transactions on Mobile Computing, 2023, 22 (4): 1968-1987. |
| [34] | Guo F, Chen X, Li X, et al. An Energy-Efficient Dynamic Scheduling Method of Deadline-Constrained Workflows in a Cloud Environment [J]. IEEE Transactions on Network and Service Management, 2022, 1-1. |
| [35] | Saleem U, Liu Y, Li Y, et al. Mobility aware joint task scheduling and resource allocation for cooperative mobile edge computing [J]. IEEE Transactions on Wireless Communications, 2020, 20 (1): 360-374. |
| [36] | 姚枝秀, 夏士超, 李云. 不确定网络环境下的任务卸载和资源分配算法 [J]. 中国科学: 信息科学, 2022, 52: 1349-1361. |
| [37] | Chen X, Zhang J, Lin B, et al. Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments [J]. IEEE Transactions on Parallel and Distributed Systems, 2022, 33 (3): 683-697. |
| [38] | Chen X, Jiao L, Li W, et al. Efficient multi-user computation offloading for mobile-edge cloud computing [J]. IEEE/ACM Transactions on Networking, 2016, 24 (5): 2795-2808. |
| [39] | Chen J, Deng Q, Yang X. Non-cooperative game algorithms for computation offloading in mobile edge computing environments [J]. Journal of Parallel and Distributed Computing, 2023, 172: 18-31. |
| [40] | 刘振鹏, 郭超, 王仕磊, 陈杰, 李小菲. 基于博弈论和启发式算法的超密集网络边缘计算卸载 [J]. 计算机工程, 2022, 48 (12): 54-61, 71. |
| [41] | Yang P, Zhang Y, Lv J. Load Optimization Based on Edge Collaboration in Software Defined Ultra-Dense Networks [J]. IEEE Access, 2020, 8: 30664-30674. |
| [42] | Sadiki A, Bentahar J, Dssouli R, et al. Deep reinforcement learning for the computation offloading in MIMO-based Edge Computing [J]. Ad Hoc Networks, 2023, 141: 103080. |
| [43] | Chen X, Liu G. Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks [J]. IEEE Internet of Things Journal, 2021, 8 (13): 10843-10856. |
| [44] | Liu Q, Shi L, Sun L, et al. Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning [J]. IEEE Transactions on Vehicular Technology, 2020, 69 (5): 5723-5728. |
| [45] | Gao Z, Hao W, Han Z, et al. Q-Learning-Based Task Offloading and Resources Optimization for a Collaborative Computing System [J]. IEEE Access, 2020, 8: 149011- 149024. |
| [46] | Huang L, Bi S, Zhang Y. Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks [J]. IEEE Transactions on Mobile Computing, 2020, 19 (11): 2581-2593. |
| [47] | Tang M, Wang V. Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems [J], IEEE Transactions on Mobile Computing, 2022, 21 (6): 1985-1997. |
| [48] | Tan L, Kuang Z, Zhao L, et al. Energy-Efficient Joint Task Offloading and Resource Allocation in OFDMA-based Collaborative Edge Computing [J]. IEEE Transactions on Wireless Communications. 2022, 21 (3): 1960-1972. |
| [49] | Qian Y, Hu L, Chen J, et al. Privacy-aware service placement for mobile edge computing via federated learning [J]. Information Sciences, 2019, 505: 562-570. |
| [50] | Min M, Xiao L, Chen Y, et al. Learning-based computation offloading for IoT devices with energy harvesting [J]. IEEE Transactions on Vehicular Technology, 2019, 68 (2): 1930-1941. |
| [51] | Liu Y, Yang C, Jiang L, et al. Intelligent edge computing for IOT-based energy management in smart cities [J]. IEEE Network, 2019, 33 (2): 111-117. |
| [52] | Yan J, Bi S, Zhang Y. Optimal offloading and resource allocation in mobile edge computing with inter user task dependency [J]. IEEE Transactions on Wireless Communications, 2020, 19 (1): 235-250. |
| [53] | Li B, Niu L, Huang X, et al. Mobility Prediction-Based Computation Offloading Handoff Strategy for Vehicular Edge Computing [J]. Journal of Electronics & Information Technology, 2020, 42 (11): 2664-2670. |
| [54] | 国家发展和改革委员会, 中央网信办, 工业和信息化部, 等. 全国一体化大数据中心协同创新体系算力枢纽实施方案 [R], 2021. |
| [55] | Yao A C. Protocols for secure computations [C] // 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982). IEEE, 1982: 160-164. |
Join the Editorial Board
We invite active, qualified and high profile scientists and researchers to join as Editorial Board Members.
Join UsBecome a Reviewer
Scholars with a strong interest in reviewing are invited to join the reviewer panel to ensure the quality of the research to be published.
Join Us