A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective
Abstract
:1. Introduction
- We elucidate the differences between the operation mode and execution paradigms of edge computing and cloud computing. We analyze each paradigm from multiple aspects, including deployment, distance, latency, computation power, and storage capacity.
- We explain the architecture of edge computing and its collaboration with the end user and the cloud. In addition, we illustrate the network architecture, which encompasses the end-user (things), edge, and cloud components. We also provide an explanation of each layer within this architecture. Moreover, we conduct a comprehensive review of the available computing resources within edge computing. Additionally, we distinguish and outline the distinctive characteristics associated with each resource type. Furthermore, we present advancements in 6G as an emerging technology and consequential impact on edge computing.
- We present a step-by-step explanation of the task scheduling procedure in edge computing and discuss why edge computing is considered a promising approach for offloading time-sensitive and data-sensitive applications.
- We explore the optimization perspectives and objectives presented in state-of-the-art papers on task scheduling and examine how each paper formulates the scheduling problem.
- We categorize the task scheduling techniques into two main categories, distinguished by their operation and execution mode. Moreover, we thoroughly examine each category, presenting a detailed discussion of their characteristics. Additionally, we clarify the advantages and disadvantages inherent in each technique. Furthermore, we construct a table that compares over fifty state-of-the-art works on task scheduling to each other, considering multiple parameters.
- We clarify which task-scheduling techniques appear promising for effectively scheduling time-sensitive applications.
2. Task Scheduling in Edge Computing
- Participants: the components of the network that collaborate on task execution, such as user, edge, and cloud, are the participants [35].
- Resources: edge computing components that provide a service in the network, such as communication resources, storage resources, caching resources, and computing resources [36].
- Methodology: different methods can be utilized to schedule tasks, including centralized and distributed [39].
- Computation offloading: Determining which tasks need to executed by edge computing.
- Resource allocation: Determining which of the edge computing server nodes is the most suitable for the task.
- User mobility: The task scheduling method should regularly check the presence of the end-user as the user might leave or join the covered area.
3. Method
3.1. Research Question
- RQ1: What techniques have been utilized for scheduling the tasks in edge computing?
- o
- Through resolving these investigations, a more comprehensive comprehension of different task scheduling methods can be achieved, facilitating an exploration of the advantages and disadvantages of each method to determine appropriate task scheduling approaches for time-sensitive applications.
- RQ2: What techniques are suitable for scheduling the tasks of time-sensitive applications?
- o
- Answering this inquiry would clarify which task-scheduling technique is more suitable for time-sensitive applications.
3.2. Inclusion Criteria
- IC1: Address the task and resource management on edge computing.
- IC2: Address the task scheduling challenge on edge computing.
- IC3: Address the implementation and development of time-sensitive applications in edge computing.
- IC4: Address task complexity in task scheduling to manage various tasks with different levels of complexity.
- IC5: Address resource availability of computational resources, including processors, memory, and storage, at edge devices into the task scheduling algorithm.
- IC6: Address latency requirements of tasks with strict latency requirements to ensure deadlines are met.
- IC7: Address the optimization of energy consumption in edge devices.
- IC8: Address the optimization of computation latency of edge devices in edge computing.
- IC9: Address security and privacy to ensure the confidentiality and integrity of the data being processed on edge devices.
- IC10: Address network bandwidth availability where data need to be transmitted in the edge network.
- IC11: Address users’ preferences, such as their desired service quality level or willingness to trade off performance for energy savings.
4. Network Architecture
4.1. Things Layer
4.2. Edge Layer
4.3. Cloud Layer
4.4. Network Resources
4.5. 6G Networks
5. Optimization Properties
5.1. Main Viewpoint
- End-user devices: the scheduling techniques consider optimizing parameters such as energy consumption, response time, or cost on the side of end-user devices.
- Edge servers: given the limited computational and storage resources of end-user devices, the proposed scheduling techniques aim to enhance the efficiency of edge servers. Specifically, these techniques strive to minimize energy consumption, improve resource utilization, and minimize costs.
- Hybrid: subsequent studies in this field have focused on hybrid scheduling techniques that optimize the parameters of both end-user devices and edge servers. These studies acknowledge that end-user devices have modest computational resources and explore the offloading of specific tasks to edge servers to improve the overall performance of the edge computing platform, including both end-user devices and edge servers.
5.2. Optimization Objective
6. RQ1: Centralized and Distributed Task Scheduling Techniques
6.1. Centralized Task Scheduling Technique
6.1.1. Convex Optimization
6.1.2. Approximation Algorithms
6.1.3. Heuristic
6.1.4. Metaheuristic
6.1.5. Machine Learning
6.2. Distributed Task Scheduling Techniques
6.2.1. Game Theory
6.2.2. Matching Theory
6.2.3. Auction
6.2.4. Distributed Machine Learning
7. RQ2: Scheduling Real-Time Embedded System Application Tasks
8. Challenges and Future Research Directions
8.1. Requirements of Realtime Systems
8.2. Dynamic Environments and Tasks Dependancy
8.3. Security and Privacy
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Ali, B.; Gregory, M.A.; Li, S. Multi-access edge computing architecture, data security and privacy: A review. IEEE Access 2021, 9, 18706–18721. [Google Scholar] [CrossRef]
- Islam, A.; Debnath, A.; Ghose, M.; Chakraborty, S. A survey on task offloading in multi-access edge computing. J. Syst. Archit. 2021, 118, 102225. [Google Scholar] [CrossRef]
- Ahmed, A.A.; Echi, M. Hawk-eye: An ai-powered threat detector for intelligent surveillance cameras. IEEE Access 2021, 9, 63283–63293. [Google Scholar] [CrossRef]
- Gupta, B. Analysis of IoT concept applications: Smart home perspective. In Proceedings of the Future Access Enablers for Ubiquitous and Intelligent Infrastructures: 5th EAI International Conference, FABULOUS 2021, Virtual Event, 6–7 May 2021; Volume 382, p. 167. [Google Scholar]
- Rana, B.; Singh, Y.; Singh, P.K. A systematic survey on internet of things: Energy efficiency and interoperability perspective. Trans. Emerg. Telecommun. Technol. 2021, 32, e4166. [Google Scholar] [CrossRef]
- Atieh, A.T. The next generation cloud technologies: A review on distributed cloud, fog and edge computing and their opportunities and challenges. Res. Rev. Sci. Technol. 2021, 1, 1–15. [Google Scholar]
- Nguyen, D.C.; Ding, M.; Pathirana, P.N.; Seneviratne, A.; Li, J.; Niyato, D.; Dobre, O.; Poor, H.V. 6G Internet of Things: A comprehensive survey. IEEE Internet Things J. 2021, 9, 359–383. [Google Scholar] [CrossRef]
- Deng, Y.; Chen, X.; Zhu, G.; Fang, Y.; Chen, Z.; Deng, X. Actions at the Edge: Jointly Optimizing the Resources in Multi-access Edge Computing. IEEE Wirel. Commun. 2022, 29, 192–198. [Google Scholar] [CrossRef]
- Busacca, F.; Galluccio, L.; Palazzo, S. Drone-assisted edge computing: A game-theoretical approach. In Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Toronto, ON, Canada, 6–9 July 2020; pp. 671–676. [Google Scholar]
- Shannigrahi, S.; Mastorakis, S.; Ortega, F.R. Next-generation networking and edge computing for mixed reality real-time interactive systems. In Proceedings of the 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 7–11 June 2020; pp. 1–6. [Google Scholar]
- Cui, M.; Zhong, S.; Li, B.; Chen, X.; Huang, K. Offloading autonomous driving services via edge computing. IEEE Internet Things J. 2020, 7, 10535–10547. [Google Scholar] [CrossRef]
- Qiu, T.; Chi, J.; Zhou, X.; Ning, Z.; Atiquzzaman, M.; Wu, D.O. Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Commun. Surv. Tutor. 2020, 22, 2462–2488. [Google Scholar] [CrossRef]
- Chen, Y.-Y.; Lin, Y.-H.; Hu, Y.-C.; Hsia, C.-H.; Lian, Y.-A.; Jhong, S.-Y. Distributed Real-Time Object Detection Based on Edge-Cloud Collaboration for Smart Video Surveillance Applications. IEEE Access 2022, 10, 93745–93759. [Google Scholar] [CrossRef]
- Hartmann, M.; Hashmi, U.S.; Imran, A. Edge computing in smart health care systems: Review, challenges, and research directions. Trans. Emerg. Telecommun. Technol. 2022, 33, e3710. [Google Scholar] [CrossRef]
- Sacco, A.; Esposito, F.; Marchetto, G. Restoring Application Traffic of Latency-Sensitive Networked Systems Using Adversarial Autoencoders. IEEE Trans. Netw. Serv. Manag. 2022, 19, 2521–2535. [Google Scholar] [CrossRef]
- Satyanarayanan, M.; Bahl, P.; Caceres, R.; Davies, N. The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput. 2009, 8, 14–23. [Google Scholar] [CrossRef]
- Ray, P.P.; Dash, D.; De, D. Edge computing for Internet of Things: A survey, e-healthcare case study and future direction. J. Netw. Comput. Appl. 2019, 140, 1–22. [Google Scholar] [CrossRef]
- Cao, K.; Hu, S.; Shi, Y.; Colombo, A.W.; Karnouskos, S.; Li, X. A survey on edge and edge-cloud computing assisted cyber-physical systems. IEEE Trans. Ind. Inform. 2021, 17, 7806–7819. [Google Scholar] [CrossRef]
- Mansouri, Y.; Babar, M.A. A review of edge computing: Features and resource virtualization. J. Parallel Distrib. Comput. 2021, 150, 155–183. [Google Scholar] [CrossRef]
- Carvalho, G.; Cabral, B.; Pereira, V.; Bernardino, J. Edge computing: Current trends, research challenges and future directions. Computing 2021, 103, 993–1023. [Google Scholar] [CrossRef]
- Shakarami, A.; Ghobaei-Arani, M.; Masdari, M.; Hosseinzadeh, M. A survey on the computation offloading approaches in mobile edge/cloud computing environment: A stochastic-based perspective. J. Grid Comput. 2020, 18, 639–671. [Google Scholar] [CrossRef]
- Liu, L.; Chen, C.; Pei, Q.; Maharjan, S.; Zhang, Y. Vehicular edge computing and networking: A survey. Mob. Netw. Appl. 2021, 26, 1145–1168. [Google Scholar] [CrossRef]
- Luo, Q.; Hu, S.; Li, C.; Li, G.; Shi, W. Resource scheduling in edge computing: A survey. IEEE Commun. Surv. Tutor. 2021, 23, 2131–2165. [Google Scholar] [CrossRef]
- Chen, S.; Li, Q.; Zhou, M.; Abusorrah, A. Recent advances in collaborative scheduling of computing tasks in an edge computing paradigm. Sensors 2021, 21, 779. [Google Scholar] [CrossRef] [PubMed]
- Mijuskovic, A.; Chiumento, A.; Bemthuis, R.; Aldea, A.; Havinga, P. Resource management techniques for cloud/fog and edge computing: An evaluation framework and classification. Sensors 2021, 21, 1832. [Google Scholar] [CrossRef] [PubMed]
- Salaht, F.A.; Desprez, F.; Lebre, A. An overview of service placement problem in fog and edge computing. ACM Comput. Surv. CSUR 2020, 53, 1–35. [Google Scholar] [CrossRef]
- Jiang, C.; Fan, T.; Gao, H.; Shi, W.; Liu, L.; Cérin, C.; Wan, J. Energy aware edge computing: A survey. Comput. Commun. 2020, 151, 556–580. [Google Scholar] [CrossRef]
- Lin, H.; Zeadally, S.; Chen, Z.; Labiod, H.; Wang, L. A survey on computation offloading modeling for edge computing. J. Netw. Comput. Appl. 2020, 169, 102781. [Google Scholar] [CrossRef]
- Goudarzi, M.; Palaniswami, M.; Buyya, R. Scheduling IoT applications in edge and fog computing environments: A taxonomy and future directions. ACM Comput. Surv. 2022, 55, 1–41. [Google Scholar] [CrossRef]
- Jia, M.; Fan, Y.; Cai, Y. A Survey on Task Scheduling Schemes in Mobile Edge Computing. In Proceedings of the Big Data and Security: Third International Conference, ICBDS 2021, Shenzhen, China, 26–28 November 2021; Proceedings. Springer: Berlin/Heidelberg, Germany, 2022; pp. 426–439. [Google Scholar]
- Avan, A.; Taheri, M.; Moaiyeri, M.H.; Navi, K. Energy-Efficient approximate compressor design for error-resilient digital signal processing. Int. J. Electron. 2022, 1–23. [Google Scholar] [CrossRef]
- Avan, A.; Maleknejad, M.; Navi, K. High-speed energy efficient process, voltage and temperature tolerant hybrid multi-threshold 4: 2 compressor design in CNFET technology. IET Circuits Devices Syst. 2020, 14, 357–368. [Google Scholar] [CrossRef]
- Busacca, F.; Grasso, C.; Palazzo, S.; Schembra, G. A smart road side unit in a microeolic box to provide edge computing for vehicular applications. IEEE Trans. Green Commun. Netw. 2022, 7, 194–210. [Google Scholar] [CrossRef]
- Hayat, S.; Jung, R.; Hellwagner, H.; Bettstetter, C.; Emini, D.; Schnieders, D. Edge computing in 5G for drone navigation: What to offload? IEEE Robot. Autom. Lett. 2021, 6, 2571–2578. [Google Scholar] [CrossRef]
- Zhang, G.; Ni, S.; Zhao, P. Learning-Based Joint Optimization of Energy Delay and Privacy in Multiple-User Edge-Cloud Collaboration MEC Systems. IEEE Internet Things J. 2021, 9, 1491–1502. [Google Scholar] [CrossRef]
- Zhang, H.; Yang, Y.; Shang, B.; Zhang, P. Joint Resource Allocation and Multi-Part Collaborative Task Offloading in MEC Systems. IEEE Trans. Veh. Technol. 2022, 71, 8877–8890. [Google Scholar] [CrossRef]
- Choi, J.D.; Kim, M.Y. A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection. ICT Express 2022, 9, 222–227. [Google Scholar] [CrossRef]
- Liang, S.; Wu, H.; Zhen, L.; Hua, Q.; Garg, S.; Kaddoum, G.; Hassan, M.M.; Yu, K. Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 2022, 23, 25345–25360. [Google Scholar] [CrossRef]
- Gao, Y.; Yang, S.; Li, F.; Trajanovski, S.; Zhou, P.; Hui, P.; Fu, X. Video Content Placement At the Network Edge: Centralized and Distributed Algorithms. IEEE Trans. Mob. Comput. 2022, 1–17. [Google Scholar] [CrossRef]
- Kitchenham, B.; Brereton, O.P.; Budgen, D.; Turner, M.; Bailey, J.; Linkman, S. Systematic literature reviews in software engineering—A systematic literature review. Inf. Softw. Technol. 2009, 51, 7–15. [Google Scholar] [CrossRef]
- Mahjoubi, A.; Grinnemo, K.-J.; Taheri, J. EHGA: A Genetic Algorithm Based Approach for Scheduling Tasks on Distributed Edge-Cloud Infrastructures. In Proceedings of the 2022 13th International Conference on Network of the Future (NoF), Ghent, Belgium, 5–7 October 2022; pp. 1–5. [Google Scholar]
- Mahjoubi, A.; Taheri, J.; Grinnemo, K.-J.; Deng, S. Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures. In Proceedings of the 2021 IEEE 46th Conference on Local Computer Networks (LCN), Edmonton, AB, Canada, 4–7 October 2021; pp. 495–502. [Google Scholar]
- Fizza, K.; Auluck, N.; Azim, A. Improving the schedulability of real-time tasks using fog computing. IEEE Trans. Serv. Comput. 2019, 15, 372–385. [Google Scholar] [CrossRef]
- Mahjoubi, A.; Grinnemo, K.-J.; Taheri, J. An Efficient Simulated Annealing-based Task Scheduling Technique for Task Offloading in a Mobile Edge Architecture. In Proceedings of the 2022 IEEE 11th International Conference on Cloud Networking (CloudNet), Paris, France, 7–10 November 2022; pp. 159–167. [Google Scholar]
- Zhu, Z.; Zhang, J.; Zhao, J.; Cao, J.; Zhao, D.; Jia, G.; Meng, Q. A hardware and software task-scheduling framework based on CPU+ FPGA heterogeneous architecture in edge computing. IEEE Access 2019, 7, 148975–148988. [Google Scholar] [CrossRef]
- Boutros, A.; Nurvitadhi, E.; Ma, R.; Gribok, S.; Zhao, Z.; Hoe, J.C.; Betz, V.; Langhammer, M. Beyond peak performance: Comparing the real performance of AI-optimized FPGAs and GPUs. In Proceedings of the 2020 International Conference on Field-Programmable Technology (ICFPT), Maui, HI, USA, 9–11 December 2020; pp. 10–19. [Google Scholar]
- Yan, L.; Cao, S.; Gong, Y.; Han, H.; Wei, J.; Zhao, Y.; Yang, S. SatEC: A 5G satellite edge computing framework based on microservice architecture. Sensors 2019, 19, 831. [Google Scholar] [CrossRef] [Green Version]
- Du, S.; Huang, T.; Hou, J.; Song, S.; Song, Y. FPGA based acceleration of game theory algorithm in edge computing for autonomous driving. J. Syst. Archit. 2019, 93, 33–39. [Google Scholar] [CrossRef]
- Cho, G.; Kim, S.-H.; Youn, C.-H. Hybrid Resource Scheduling Scheme for Video Surveillance in GPU-FPGA Accelerated Edge Computing System. In Advances in Artificial Intelligence and Applied Cognitive Computing; Springer: Berlin/Heidelberg, Germany, 2021; pp. 679–694. [Google Scholar]
- Simon, B. Scheduling Task Graphs on Modern Computing Platforms. Ph.D. Thesis, Université de Lyon, Lyon, France, 2018. [Google Scholar]
- You, X.; Huang, Y.; Liu, S.; Wang, D.; Ma, J.; Xu, W.; Zhang, C.; Zhan, H.; Zhang, C.; Zhang, J. Toward 6G TK $\mu $ Extreme Connectivity: Architecture, Key Technologies and Experiments. arXiv 2022, arXiv:2208.01190. [Google Scholar]
- Wei, P.; Guo, K.; Li, Y.; Wang, J.; Feng, W.; Jin, S.; Ge, N.; Liang, Y.-C. Reinforcement learning-empowered mobile edge computing for 6G edge intelligence. IEEE Access 2022, 10, 65156–65192. [Google Scholar] [CrossRef]
- He, J.; Guo, S.; Li, M.; Zhu, Y. AceFL: Federated Learning Accelerating in 6G-enabled Mobile Edge Computing Networks. IEEE Trans. Netw. Sci. Eng. 2022, 10, 1364–1375. [Google Scholar] [CrossRef]
- Goudarzi, M. Energy and Time Aware Scheduling of Applications in Edge and Fog Computing Environments. 2022. Available online: https://www.researchgate.net/publication/361431337_Energy_and_Time_Aware_Scheduling_of_Applications_in_Edge_and_Fog_Computing_Environments (accessed on 8 May 2023).
- Atoui, W.S.; Ajib, W.; Boukadoum, M. Offline and online scheduling algorithms for energy harvesting RSUs in VANETs. IEEE Trans. Veh. Technol. 2018, 67, 6370–6382. [Google Scholar] [CrossRef]
- Liu, H.; Long, X.; Li, Z.; Long, S.; Rong, R.; Wang, H.-M. Joint Optimization of Request Assignment and Computing Resource Allocation in Multi-Access Edge Computing. IEEE Trans. Serv. Comput. 2022, 16, 1254–1267. [Google Scholar] [CrossRef]
- Chen, L.; Zhou, S.; Xu, J. Computation peer offloading for energy-constrained mobile edge computing in small-cell networks. IEEE/ACM Trans. Netw. 2018, 26, 1619–1632. [Google Scholar] [CrossRef] [Green Version]
- He, X.; Jin, R.; Dai, H. Peace: Privacy-preserving and cost-efficient task offloading for mobile-edge computing. IEEE Trans. Wirel. Commun. 2019, 19, 1814–1824. [Google Scholar] [CrossRef]
- Zhang, Q.; Gui, L.; Hou, F.; Chen, J.; Zhu, S.; Tian, F. Dynamic task offloading and resource allocation for mobile-edge computing in dense cloud RAN. IEEE Internet Things J. 2020, 7, 3282–3299. [Google Scholar] [CrossRef]
- Li, C.; Tang, J.; Luo, Y. Dynamic multi-user computation offloading for wireless powered mobile edge computing. J. Netw. Comput. Appl. 2019, 131, 1–15. [Google Scholar] [CrossRef]
- Saleem, U.; Liu, Y.; Jangsher, S.; Tao, X.; Li, Y. Latency minimization for D2D-enabled partial computation offloading in mobile edge computing. IEEE Trans. Veh. Technol. 2020, 69, 4472–4486. [Google Scholar] [CrossRef]
- Zhong, X.; Wang, X.; Yang, T.; Yang, Y.; Qin, Y.; Ma, X. POTAM: A parallel optimal task allocation mechanism for large-scale delay sensitive mobile edge computing. IEEE Trans. Commun. 2022, 70, 2499–2517. [Google Scholar] [CrossRef]
- Liang, J.; Ma, B.; Feng, Z.; Huang, J. Reliability-aware Task Processing and Offloading for Data-intensive Applications in Edge computing. IEEE Trans. Netw. Serv. Manag. 2023, 1. [Google Scholar] [CrossRef]
- Deng, Y.; Chen, Z.; Yao, X.; Hassan, S.; Ibrahim, A.M. Parallel offloading in green and sustainable mobile edge computing for delay-constrained IoT system. IEEE Trans. Veh. Technol. 2019, 68, 12202–12214. [Google Scholar] [CrossRef]
- Pasteris, S.; Wang, S.; Herbster, M.; He, T. Service placement with provable guarantees in heterogeneous edge computing systems. In Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications, Paris, France, 29 April–2 May 2019; pp. 514–522. [Google Scholar]
- Lu, S.; Wu, J.; Duan, Y.; Wang, N.; Fang, J. Cost-efficient resource provision for multiple mobile users in fog computing. In Proceedings of the 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), Tianjin, China, 4–6 December 2019; pp. 422–429. [Google Scholar]
- Meng, X.; Wang, W.; Wang, Y.; Lau, V.K.; Zhang, Z. Closed-form delay-optimal computation offloading in mobile edge computing systems. IEEE Trans. Wirel. Commun. 2019, 18, 4653–4667. [Google Scholar] [CrossRef] [Green Version]
- Guo, Y.; Wang, S.; Zhou, A.; Xu, J.; Yuan, J.; Hsu, C.H. User allocation-aware edge cloud placement in mobile edge computing. Softw. Pract. Exp. 2020, 50, 489–502. [Google Scholar] [CrossRef]
- Stavrinides, G.L.; Karatza, H.D. A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimed. Tools Appl. 2019, 78, 24639–24655. [Google Scholar] [CrossRef]
- Azizi, S.; Shojafar, M.; Abawajy, J.; Buyya, R. Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach. J. Netw. Comput. Appl. 2022, 201, 103333. [Google Scholar] [CrossRef]
- Hu, S.; Li, G.; Shi, W. Lars: A latency-aware and real-time scheduling framework for edge-enabled internet of vehicles. IEEE Trans. Serv. Comput. 2021, 16, 398–411. [Google Scholar] [CrossRef]
- Meng, J.; Tan, H.; Xu, C.; Cao, W.; Liu, L.; Li, B. Dedas: Online task dispatching and scheduling with bandwidth constraint in edge computing. In Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications, Paris, France, 29 April–2 May 2019; pp. 2287–2295. [Google Scholar]
- Chiang, Y.-H.; Zhang, T.; Ji, Y. Joint cotask-aware offloading and scheduling in mobile edge computing systems. IEEE Access 2019, 7, 105008–105018. [Google Scholar] [CrossRef]
- Ben Salah, N.; Bellamine Ben Saoud, N. An IoT-oriented Multiple Data Replicas Placement Strategy in Hybrid Fog-Cloud Environment. In Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Virtual Event, 31 May–2 June 2021; pp. 119–128. [Google Scholar]
- Hoseiny, F.; Azizi, S.; Shojafar, M.; Ahmadiazar, F.; Tafazolli, R. PGA: A priority-aware genetic algorithm for task scheduling in heterogeneous fog-cloud computing. In Proceedings of the IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, BC, Canada, 10–13 May 2021; pp. 1–6. [Google Scholar]
- Maia, A.M.; Ghamri-Doudane, Y.; Vieira, D.; de Castro, M.F. An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing. Comput. Netw. 2021, 194, 108146. [Google Scholar] [CrossRef]
- Aburukba, R.O.; Landolsi, T.; Omer, D. A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices. J. Netw. Comput. Appl. 2021, 180, 102994. [Google Scholar] [CrossRef]
- Xu, X.; Liu, Q.; Luo, Y.; Peng, K.; Zhang, X.; Meng, S.; Qi, L. A computation offloading method over big data for IoT-enabled cloud-edge computing. Future Gener. Comput. Syst. 2019, 95, 522–533. [Google Scholar] [CrossRef]
- Peng, K.; Zhu, M.; Zhang, Y.; Liu, L.; Zhang, J.; Leung, V.; Zheng, L. An energy-and cost-aware computation offloading method for workflow applications in mobile edge computing. EURASIP J. Wirel. Commun. Netw. 2019, 2019, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Xu, X.; Cao, H.; Geng, Q.; Liu, X.; Dai, F.; Wang, C. Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment. Concurr. Comput. Pract. Exp. 2022, 34, e5674. [Google Scholar] [CrossRef]
- Hu, S.; Li, G. Dynamic request scheduling optimization in mobile edge computing for IoT applications. IEEE Internet Things J. 2019, 7, 1426–1437. [Google Scholar] [CrossRef]
- Xu, X.; Li, Y.; Huang, T.; Xue, Y.; Peng, K.; Qi, L.; Dou, W. An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks. J. Netw. Comput. Appl. 2019, 133, 75–85. [Google Scholar] [CrossRef]
- Mseddi, A.; Jaafar, W.; Elbiaze, H.; Ajib, W. Joint container placement and task provisioning in dynamic fog computing. IEEE Internet Things J. 2019, 6, 10028–10040. [Google Scholar] [CrossRef]
- Wu, Y.; Wu, J.; Chen, L.; Yan, J.; Luo, Y. Efficient task scheduling for servers with dynamic states in vehicular edge computing. Comput. Commun. 2020, 150, 245–253. [Google Scholar] [CrossRef]
- Kishor, A.; Chakarbarty, C. Task offloading in fog computing for using smart ant colony optimization. Wirel. Pers. Commun. 2021, 127, 1683–1704. [Google Scholar] [CrossRef]
- Huang, P.-Q.; Wang, Y.; Wang, K.; Liu, Z.-Z. A bilevel optimization approach for joint offloading decision and resource allocation in cooperative mobile edge computing. IEEE Trans. Cybern. 2019, 50, 4228–4241. [Google Scholar] [CrossRef] [Green Version]
- Hussain, M.; Azar, A.T.; Ahmed, R.; Umar Amin, S.; Qureshi, B.; Dinesh Reddy, V.; Alam, I.; Khan, Z.I. SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks. Sensors 2023, 23, 667. [Google Scholar] [CrossRef]
- Guo, F.; Zhang, H.; Ji, H.; Li, X.; Leung, V.C. An efficient computation offloading management scheme in the densely deployed small cell networks with mobile edge computing. IEEE/ACM Trans. Netw. 2018, 26, 2651–2664. [Google Scholar] [CrossRef]
- Chen, W.; Wang, D.; Li, K. Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans. Serv. Comput. 2018, 12, 726–738. [Google Scholar] [CrossRef]
- Bahreini, T.; Brocanelli, M.; Grosu, D. VECMAN: A framework for energy-aware resource management in vehicular edge computing systems. IEEE Trans. Mob. Comput. 2021, 22, 1231–1245. [Google Scholar] [CrossRef]
- Jiang, F.; Wang, K.; Dong, L.; Pan, C.; Xu, W.; Yang, K. Deep-learning-based joint resource scheduling algorithms for hybrid MEC networks. IEEE Internet Things J. 2019, 7, 6252–6265. [Google Scholar] [CrossRef] [Green Version]
- Huang, L.; Feng, X.; Feng, A.; Huang, Y.; Qian, L.P. Distributed deep learning-based offloading for mobile edge computing networks. Mob. Netw. Appl. 2018, 27, 1123–1130. [Google Scholar] [CrossRef]
- Sheng, S.; Chen, P.; Chen, Z.; Wu, L.; Yao, Y. Deep reinforcement learning-based task scheduling in iot edge computing. Sensors 2021, 21, 1666. [Google Scholar] [CrossRef]
- Jiang, F.; Wang, K.; Dong, L.; Pan, C.; Yang, K. Stacked autoencoder-based deep reinforcement learning for online resource scheduling in large-scale MEC networks. IEEE Internet Things J. 2020, 7, 9278–9290. [Google Scholar] [CrossRef] [Green Version]
- Watkins, C.J.; Dayan, P. Q-learning. Mach. Learn. 1992, 8, 279–292. [Google Scholar] [CrossRef]
- Qiu, X.; Liu, L.; Chen, W.; Hong, Z.; Zheng, Z. Online deep reinforcement learning for computation offloading in blockchain-empowered mobile edge computing. IEEE Trans. Veh. Technol. 2019, 68, 8050–8062. [Google Scholar] [CrossRef]
- Nguyen, T.T.; Nguyen, N.D.; Nahavandi, S. Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications. IEEE Trans. Cybern. 2020, 50, 3826–3839. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, X.; Yu, J.; Wang, J.; Gao, Y. Resource allocation with edge computing in IoT networks via machine learning. IEEE Internet Things J. 2020, 7, 3415–3426. [Google Scholar] [CrossRef]
- Wang, J.; Hu, J.; Min, G.; Zhan, W.; Ni, Q.; Georgalas, N. Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning. IEEE Commun. Mag. 2019, 57, 64–69. [Google Scholar] [CrossRef] [Green Version]
- Zhang, K.; Zhu, Y.; Leng, S.; He, Y.; Maharjan, S.; Zhang, Y. Deep learning empowered task offloading for mobile edge computing in urban informatics. IEEE Internet Things J. 2019, 6, 7635–7647. [Google Scholar] [CrossRef]
- Xiong, X.; Zheng, K.; Lei, L.; Hou, L. Resource allocation based on deep reinforcement learning in IoT edge computing. IEEE J. Sel. Areas Commun. 2020, 38, 1133–1146. [Google Scholar] [CrossRef]
- Zhai, Y.; Bao, T.; Zhu, L.; Shen, M.; Du, X.; Guizani, M. Toward reinforcement-learning-based service deployment of 5G mobile edge computing with request-aware scheduling. IEEE Wirel. Commun. 2020, 27, 84–91. [Google Scholar] [CrossRef]
- Lu, H.; Gu, C.; Luo, F.; Ding, W.; Liu, X. Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Gener. Comput. Syst. 2020, 102, 847–861. [Google Scholar] [CrossRef]
- Shen, S.; Han, Y.; Wang, X.; Wang, Y. Computation offloading with multiple agents in edge-computing–supported IoT. ACM Trans. Sens. Netw. TOSN 2019, 16, 1–27. [Google Scholar] [CrossRef]
- Li, Q.; Zhao, J.; Gong, Y. Cooperative computation offloading and resource allocation for mobile edge computing. In Proceedings of the 2019 IEEE International Conference on Communications Workshops (ICC Workshops), Shanghai, China, 20–24 May 2019; pp. 1–6. [Google Scholar]
- Ranadheera, S.; Maghsudi, S.; Hossain, E. Computation offloading and activation of mobile edge computing servers: A minority game. IEEE Wirel. Commun. Lett. 2018, 7, 688–691. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Xia, W.; Yan, F.; Shen, L. Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access 2018, 6, 19324–19337. [Google Scholar] [CrossRef]
- Asheralieva, A.; Niyato, D. Hierarchical game-theoretic and reinforcement learning framework for computational offloading in UAV-enabled mobile edge computing networks with multiple service providers. IEEE Internet Things J. 2019, 6, 8753–8769. [Google Scholar] [CrossRef]
- Smys, S.; Ranganathan, G. Performance evaluation of game theory based efficient task scheduling for edge computing. J. ISMAC 2020, 2, 50–61. [Google Scholar]
- Teng, H.; Li, Z.; Cao, K.; Long, S.; Guo, S.; Liu, A. Game theoretical task offloading for profit maximization in mobile edge computing. IEEE Trans. Mob. Comput. 2022, 1. [Google Scholar] [CrossRef]
- Gu, B.; Zhou, Z. Task offloading in vehicular mobile edge computing: A matching-theoretic framework. IEEE Veh. Technol. Mag. 2019, 14, 100–106. [Google Scholar] [CrossRef]
- Chiti, F.; Fantacci, R.; Paganelli, F.; Picano, B. Virtual functions placement with time constraints in fog computing: A matching theory perspective. IEEE Trans. Netw. Serv. Manag. 2019, 16, 980–989. [Google Scholar] [CrossRef]
- Gu, B.; Zhou, Z.; Mumtaz, S.; Frascolla, V.; Bashir, A.K. Context-aware task offloading for multi-access edge computing: Matching with externalities. In Proceedings of the 2018 IEEE global communications conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 9–13 December 2018; pp. 1–6. [Google Scholar]
- Pham, Q.-V.; Leanh, T.; Tran, N.H.; Park, B.J.; Hong, C.S. Decentralized computation offloading and resource allocation for mobile-edge computing: A matching game approach. IEEE Access 2018, 6, 75868–75885. [Google Scholar] [CrossRef]
- Zhang, D.; Tan, L.; Ren, J.; Awad, M.K.; Zhang, S.; Zhang, Y.; Wan, P.-J. Near-optimal and truthful online auction for computation offloading in green edge-computing systems. IEEE Trans. Mob. Comput. 2019, 19, 880–893. [Google Scholar] [CrossRef]
- Ma, L.; Wang, X.; Wang, X.; Wang, L.; Shi, Y.; Huang, M. TCDA: Truthful combinatorial double auctions for mobile edge computing in industrial Internet of Things. IEEE Trans. Mob. Comput. 2021, 21, 4125–4138. [Google Scholar] [CrossRef]
- Peng, X.; Ota, K.; Dong, M. Multiattribute-based double auction toward resource allocation in vehicular fog computing. IEEE Internet Things J. 2020, 7, 3094–3103. [Google Scholar] [CrossRef]
- Zhou, H.; Wu, T.; Chen, X.; He, S.; Guo, D.; Wu, J. Reverse auction-based computation offloading and resource allocation in mobile cloud-edge computing. IEEE Trans. Mob. Comput. 2022, 1–15. [Google Scholar] [CrossRef]
- He, J.; Zhang, D.; Zhou, Y.; Zhang, Y. A truthful online mechanism for collaborative computation offloading in mobile edge computing. IEEE Trans. Ind. Inform. 2019, 16, 4832–4841. [Google Scholar] [CrossRef]
- Liu, S.; Zheng, C.; Huang, Y.; Quek, T.Q. Distributed reinforcement learning for privacy-preserving dynamic edge caching. IEEE J. Sel. Areas Commun. 2022, 40, 749–760. [Google Scholar] [CrossRef]
- Zheng, C.; Liu, S.; Huang, Y.; Zhang, W.; Yang, L. Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile-Edge Computing Networks. IEEE Internet Things J. 2022, 9, 24328–24345. [Google Scholar] [CrossRef]
- Nguyen, D.C.; Ding, M.; Pham, Q.-V.; Pathirana, P.N.; Le, L.B.; Seneviratne, A.; Li, J.; Niyato, D.; Poor, H.V. Federated learning meets blockchain in edge computing: Opportunities and challenges. IEEE Internet Things J. 2021, 8, 12806–12825. [Google Scholar] [CrossRef]
- Wang, R.; Lai, J.; Zhang, Z.; Li, X.; Vijayakumar, P.; Karuppiah, M. Privacy-preserving federated learning for internet of medical things under edge computing. IEEE J. Biomed. Health Inform. 2022, 27, 854–865. [Google Scholar] [CrossRef]
- Lakhan, A.; Mohammed, M.A.; Kadry, S.; AlQahtani, S.A.; Maashi, M.S.; Abdulkareem, K.H. Federated learning-aware multi-objective modeling and blockchain-enable system for IIoT applications. Comput. Electr. Eng. 2022, 100, 107839. [Google Scholar] [CrossRef]
- Shi, T.; Tian, H.; Zhang, T.; Loo, J.; Ou, J.; Fan, C.; Yang, D. Task Scheduling with Collaborative Computing of MEC System Based on Federated Learning. In Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 19–22 June 2022; pp. 1–5. [Google Scholar]
- Zhang, Y.; Zhang, X.; Cai, Y. Multi-task Federated Learning based on Client Scheduling in Mobile Edge Computing. In Proceedings of the 2022 IEEE/CIC International Conference on Communications in China (ICCC), Foshan, China, 11–13 August 2022; pp. 185–190. [Google Scholar]
- Zhang, L.; Wu, S.; Xu, H.; Liu, Q.; Hong, C.S.; Han, Z. Federated Learning Over the Industrial Internet of Things: A Joint Optimization of Edge Association and Resource Allocation. 2022. Available online: https://www.techrxiv.org/articles/preprint/Federated_Learning_Over_the_Industrial_Internet_of_Things_A_Joint_Optimization_of_Edge_Association_and_Resource_Allocation/20784001 (accessed on 8 May 2023).
- Sun, F.; Zhang, Z.; Zeadally, S.; Han, G.; Tong, S. Edge Computing-Enabled Internet of Vehicles: Towards Federated Learning Empowered Scheduling. IEEE Trans. Veh. Technol. 2022, 71, 10088–10103. [Google Scholar] [CrossRef]
- Shahidinejad, A.; Farahbakhsh, F.; Ghobaei-Arani, M.; Malik, M.H.; Anwar, T. Context-aware multi-user offloading in mobile edge computing: A federated learning-based approach. J. Grid Comput. 2021, 19, 1–23. [Google Scholar] [CrossRef]
- Lu, H.; He, X.; Du, M.; Ruan, X.; Sun, Y.; Wang, K. Edge QoE: Computation offloading with deep reinforcement learning for Internet of Things. IEEE Internet Things J. 2020, 7, 9255–9265. [Google Scholar] [CrossRef]
- Wang, J.; Hu, J.; Min, G.; Zhan, W.; Zomaya, A.Y.; Georgalas, N. Dependent task offloading for edge computing based on deep reinforcement learning. IEEE Trans. Comput. 2021, 71, 2449–2461. [Google Scholar] [CrossRef]
- Yeganeh, S.; Sangar, A.B.; Azizi, S. A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments. J. Netw. Comput. Appl. 2023, 214, 103617. [Google Scholar] [CrossRef]
- Cheng, Z.; Min, M.; Liwang, M.; Huang, L.; Gao, Z. Multiagent DDPG-based joint task partitioning and power control in Fog computing networks. IEEE Internet Things J. 2021, 9, 104–116. [Google Scholar] [CrossRef]
- Cheng, Z.; Liwang, M.; Chen, N.; Huang, L.; Du, X.; Guizani, M. Deep reinforcement learning-based joint task and energy offloading in UAV-aided 6G intelligent edge networks. Comput. Commun. 2022, 192, 234–244. [Google Scholar] [CrossRef]
- Zhou, X.; Liang, W.; Yan, K.; Li, W.; Kevin, I.; Wang, K.; Ma, J.; Jin, Q. Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything. IEEE Internet Things J. 2022, 10, 3295–3304. [Google Scholar] [CrossRef]
- Chen, Y.; Gu, W.; Li, K. Dynamic task offloading for internet of things in mobile edge computing via deep reinforcement learning. Int. J. Commun. Syst. 2022, e5154. [Google Scholar] [CrossRef]
- Verbraeken, J.; Wolting, M.; Katzy, J.; Kloppenburg, J.; Verbelen, T.; Rellermeyer, J.S. A survey on distributed machine learning. Acm Comput. Surv. Csur 2020, 53, 1–33. [Google Scholar] [CrossRef] [Green Version]
- Kopetz, H.; Steiner, W. Real-Time Systems: Design Principles for Distributed Embedded Applications; Springer Nature: Berlin/Heidelberg, Germany, 2022. [Google Scholar]
- Alwarafy, A.; Al-Thelaya, K.A.; Abdallah, M.; Schneider, J.; Hamdi, M. A survey on security and privacy issues in edge-computing-assisted internet of things. IEEE Internet Things J. 2020, 8, 4004–4022. [Google Scholar] [CrossRef]
- Yahuza, M.; Idris, M.Y.I.B.; Wahab, A.W.B.A.; Ho, A.T.; Khan, S.; Musa, S.N.B.; Taha, A.Z.B. Systematic review on security and privacy requirements in edge computing: State of the art and future research opportunities. IEEE Access 2020, 8, 76541–76567. [Google Scholar] [CrossRef]
Ref. | Edge Computing | Resource and Task Management | Real-Time Perspective | Summary |
---|---|---|---|---|
[17] | Yes | No | No | Presents a classification of industrial aspects benefiting from IoT and edge computing. Proposes two real-world use cases that address urban smart living challenges and proposes a new architecture based on edge-IoT for e-healthcare. |
[18] | Yes | No | No | Explains the issues in the collaboration between edge computing and CPS, reviewing recent papers focusing on and classifying QoS optimization. |
[14] | Yes | No | No | Reviews the research on the collaboration between edge computing and healthcare applications, focusing on architecture and techniques. Discusses the challenges of healthcare applications in edge computing and provide an overview of all data operations. |
[6] | Yes | No | No | Investigates edge computing as a next-generation computing technology. Elaborates on how edge computing can reduce operating costs and enhance security. Analyzes the aspects of data transmission and communication within edge computing. |
[1] | Yes | No | No | Overview of edge computing architecture, applications, and security includes the analysis of potential security risks and vulnerabilities. Several protection methods are explored to mitigate security threats. |
[19] | Yes | No | No | Examines virtualization techniques in computation and networking resources and explore their deployment in edge computing. Investigates the relationship between virtualization techniques and the requirements of IoT services. |
[20] | Yes | No | No | Explains the definitions and core characteristics of edge computing and investigates different application scenarios. |
[21] | Yes | Yes | No | Research works on task offloading are analyzed from a stochastic perspective, and a taxonomy comprising Markov chains, Markov processes, and hidden Markov models is presented. |
[22] | Yes | Yes | No | Reviews recent research on VEC regarding different aspects, presents various VEC applications, and categorize them. |
[23] | Yes | Yes | No | Reviews the papers on resource management in edge computing, wherein different aspects of resource management are explained, including computation offloading, resource allocation, and resource provisioning. |
[24] | Yes | Yes | No | Examines various task scheduling methods in the context of edge computing and explores the relationship between these methods and their corresponding problem formulations. |
[25] | Yes | Yes | No | Reviews resource management methods suitable for cloud, edge, and fog environments. Proposes an assessment framework comprising measurements for resource management algorithms in edge computing. |
[26] | Yes | Yes | No | Reviews the research progress made in edge computing regarding the service placement problem (SPP). Categorizes the various methods employed for task scheduling and other aspects associated with SPP. |
[2] | Yes | Yes | No | Reviews recent research progress in task offloading techniques for edge computing. |
[27] | Yes | Yes | No | Reviews the progress made on energy-aware aspects of edge computing in different domains, including task management. |
[23] | Yes | Yes | No | Explains the edge computing architecture and its collaboration with different task scheduling algorithms and classify recent research on resource management in edge computing. Divides the scheduling algorithms based on their operation mode. |
[12] | Yes | Yes | No | Explains the collaboration between IIoT and edge computing, as well as the related research progress. Provides a review of the advancements achieved in various technical aspects of edge computing, including task scheduling. |
[28] | Yes | Yes | No | Provides an overview of the advancements in computation offloading and categorizes computation offloading models into different classes. Explains the fundamental concepts of computation offloading and discuss various methods utilized in it. |
[29] | Yes | Yes | No | Provides a taxonomy of recent task scheduling algorithms in edge/fog computing. |
[30] | Yes | Yes | No | Reviews the recent research progress of task scheduling algorithms in edge computing, categorizing them based on task dependency and the number of available servers. |
This paper | Yes | Yes | Yes | A comprehensive survey examines the recent progress in task scheduling algorithms. The algorithms are categorized based on their operation mode, problem formulation method, and their suitability for time-sensitive applications. |
Acronym | Definition |
---|---|
WAN | Wide Area Network |
QoS | Quality of Service |
IoT | Internet of Things |
QoE | Quality of Experience |
AR | Augmented Reality |
VR | Virtual Reality |
CAD | Connected and Autonomous Driving |
SLAM | Simultaneous Localization and Mapping |
RSU | Roadside Unit |
UAV | Unmanned Aerial Vehicle |
WBAN | Wireless Body Area Networks |
RQ | Research Question |
IC | Inclusion Criteria |
LTE | Long-Term Evolution |
Wi-Fi | Wireless Fidelity |
CPU | Central Processing Unit |
GPU | Graphics Processing Unit |
ASIC | Application Specific Integrated Circuit |
FPGA | Field Programmable Gate Array |
DAG | Directed Acyclic Graph |
XR | Extended Reality |
AI | Artificial Intelligence |
ML | Machine Learning |
ILP | Integer Linear Programming |
MILP | Mixed Integer Linear Programming |
MINLP | Mixed Integer Non-Linear Programming |
MDP | Markov Decision Process |
ADMM | Alternating Direction Method of Multipliers |
EDF | Earliest Deadline First |
FCFS | First Come First Serve |
NSGA | Non-dominated Sorting Genetic Algorithm |
MOWO | Multi-Objective Whale Optimization |
SLA | Service Level Agreement |
DRL | Deep Reinforcement Learning |
ASA | Simulated Annealing Approach |
DQN | Deep Q-learning Network |
FL | Federated Learning |
LSTM | Long Short-Term Memory |
MAML | Model-agnostic Meta-learning |
IIoT | Industrial Internet of Things |
IoV | Internet of Vehicles |
Characteristic | Cloud Computing | Edge Computing |
---|---|---|
Deployment | Centralized | Distributed |
Distance | High | Low |
Latency | High | Low |
Computation power | Ample | Limited |
Storage capacity | Ample | Limited |
Processor | Characteristic |
---|---|
GPU | High latency High power consumption High flexibility |
ASIC | Low latency Low power consumption Low flexibility |
FPGA | Low latency Low power consumption High flexibility |
Technique | Operation Manner | Advantages | Disadvantages |
---|---|---|---|
Convex optimization | Centralized |
|
|
Approximation | Centralized |
|
|
Heuristic methods | Centralized |
|
|
Meta-heuristic methods | Centralized |
|
|
Machine Learning | Centralized |
|
|
Game Theory | Distributed |
|
|
Matching Theory | Distributed |
|
|
Federated Learning | Distributed |
|
|
Reference | Main Viewpoint | Optimization Goal | Objective Number | Modeling Problem | Utilized Technique | Applicable for Real-Time Task Scheduling |
---|---|---|---|---|---|---|
[57] | edge servers | Energy | Single | MINLP | Convex optimization (Lyapunov technique) | No |
[58] | End-user devices | Privacy, Energy | Multiple | ILP | Convex optimization (Lyapunov technique) | No |
[59] | edge servers | Energy | Single | MINLP | Convex optimization (Lyapunov technique) | No |
[60] | edge servers | Time, Energy, Data transmission | Multiple | MINLP | Convex optimization (Lyapunov technique) | No |
[90] | End-user devices | Energy | Single | MILP | Meta-heuristic (genetic algorithm) | No |
[69] | End-user devices | Time | Single | MILP | Heuristic (EDF) | Yes |
[130] | End-user devices | QoE | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[77] | End-user devices | Time | Single | MIP | Meta-heuristic (genetic algorithm) | Yes |
[131] | End-user devices | Energy, Time, Cost | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[89] | edge servers | Energy | Multiple | MILP | Meta-heuristic (Whale Optimization Algorithm) | No |
[75] | edge servers | Energy, Time | Multiple | ILP | Meta-heuristic (genetic algorithm) | Yes |
[132] | End-user devices | Energy, Time | Multiple | ILP | Machine learning (deep reinforcement learning) | Yes |
[70] | edge servers | Energy | Single | MINLP | Heuristic (semi-greedy) | Yes |
[92] | Hybrid | Energy, Time | Multiple | MILP | Machine learning (deep learning) | No |
[91] | End-user devices | Energy | Single | MINLP | Machine learning (deep learning) | Yes |
[94] | End-user devices | Time | Single | MINLP | Machine learning (deep reinforcement learning) | No |
[71] | End-user devices | Time | Single | MILP | Heuristic (Greedy Algorithm) | Yes |
[76] | Hybrid | Time, Cost | Multiple | MINLP | Meta-heuristic (genetic algorithm) | No |
[133] | End-user devices | Energy, Time | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[134] | Hybrid | Time, Energy | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[135] | Hybrid | Energy, QoS | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[136] | End-user devices | Energy, Task finish ratio | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[61] | End-user devices | Time | Single | MINLP | Heuristic | No |
[63] | edge server | Time | Single | MILP | Heuristic | No |
[62] | edge server | Time, Cost | Multiple | MILP | Heuristic | No |
[64] | edge server | Energy, Time | Multiple | ILP | Heuristic (Lyapunov) | Yes |
[65] | edge server | Cost | Single | MILP | Approximation | No |
[73] | Hybrid | Energy | Single | MILP | Heuristic (variation of FCFS) | No |
[74] | edge server | Time, Energy | Multiple | MILP | Heuristic (Greedy Algorithm) | No |
[87] | edge server | Time, Energy, Cost | Multiple | MINLP | Meta-heuristic (Evolutionary Algorithm) | No |
[76] | Hybrid | Service Level Agreement | Multiple | MINLP | Meta-heuristic (Genetic Algorithm) | No |
[85] | End-user devices | Time | Single | ILP | Meta-heuristic (Ant colony) | No |
[86] | End-user devices | Energy | Single | MILP | Meta-heuristic (Ant colony) | No |
[88] | End-user devices | Energy | Single | MINLP | Meta-heuristic (Genetic algorithm + Particle swarm optimization) | No |
[78] | End-user devices | Time, Energy | Multiple | MILP | Meta-heuristic (NSGA-III) | No |
[72] | End-user devices | Time | Single | ILP | Heuristic (EDF) | Yes |
[59] | edge servers | Energy | Single | MINLP | Convex optimization (Lyapunov technique) | No |
[60] | edge servers | Time, Energy, Data transmission | Multiple | MINLP | Convex optimization (Lyapunov technique) | No |
[90] | End-user devices | Energy | Single | MILP | Meta-heuristic (genetic algorithm) | No |
[69] | End-user devices | Time | Single | MILP | Heuristic (EDF) | Yes |
[130] | End-user devices | QoE | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[77] | End-user devices | Time | Single | MIP | Meta-heuristic (genetic algorithm) | Yes |
[131] | End-user devices | Energy, Time, Cost | Multiple | MDP | Machine learning (deep reinforcement learning) | No |
[89] | edge servers | Energy | Multiple | MILP | Meta-heuristic (Whale Optimization Algorithm) | No |
[75] | edge servers | Energy, Time | Multiple | ILP | Meta-heuristic (genetic algorithm) | Yes |
[132] | End-user devices | Energy, Time | Multiple | ILP | Machine learning (deep reinforcement learning) | Yes |
[70] | edge servers | Energy | Single | MINLP | Heuristic (semi-greedy) | Yes |
[92] | Hybrid | Energy, Time | Multiple | MILP | Machine learning (deep learning) | No |
[91] | End-user devices | Energy | Single | MINLP | Machine learning (deep learning) | Yes |
[94] | End-user devices | Time | Single | MINLP | Machine learning (deep reinforcement learning) | No |
[71] | End-user devices | Time | Single | MILP | Heuristic (Greedy Algorithm) | Yes |
[76] | Hybrid | Time, Cost | Multiple | MINLP | Meta-heuristic (genetic algorithm) | No |
[85] | End-user devices | Time | Single | ILP | Meta-heuristic (Ant colony) | No |
[86] | End-user devices | Energy | Single | MILP | Meta-heuristic (Ant colony) | No |
[88] | End-user devices | Energy | Single | MINLP | Meta-heuristic (Genetic algorithm + Particle swarm optimization) | No |
[78] | End-user devices | Time, Energy | Multiple | MILP | Meta-heuristic (NSGA-III) | No |
[72] | End-user devices | Time | Single | ILP | Heuristic (EDF) | Yes |
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Avan, A.; Azim, A.; Mahmoud, Q.H. A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective. Electronics 2023, 12, 2599. https://doi.org/10.3390/electronics12122599
Avan A, Azim A, Mahmoud QH. A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective. Electronics. 2023; 12(12):2599. https://doi.org/10.3390/electronics12122599
Chicago/Turabian StyleAvan, Amin, Akramul Azim, and Qusay H. Mahmoud. 2023. "A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective" Electronics 12, no. 12: 2599. https://doi.org/10.3390/electronics12122599