A Systematic Review of Optimization Techniques for Computation Offloading in Mobile Edge Computing: Lyapunov, Convex, Heuristic, Game-Theoretic, and Machine Learning Approaches
Abstract
Mobile Edge Computing (MEC) has become a key paradigm for reducing latency, energy consumption, and system overhead in computation-intensive applications by enabling task offloading to edge servers. In this paper, we conduct a Systematic Literature Review (SLR) of 70 peer-reviewed studies, with 30 papers coded in detail across five main optimization approaches: Lyapunov, convex, heuristic, game-theoretic, and machine learning methods. The review systematically compares these paradigms in terms of optimization objectives, task assumptions, and evaluation setups. Our analysis reveals that while Lyapunov and game-theoretic approaches rely entirely on simulation (100%), heuristic studies exhibit stronger practical grounding, with 33% including real-world validation. Convex methods demonstrated 10–18% energy savings in heterogeneous MEC scenarios, whereas machine learning approaches showed adaptability under uncertain conditions but lacked real-world testing. Beyond numerical comparison, the review highlights the methodological evolution of MEC optimization, from formal mathematical programming to learning-driven and hybrid approaches. The coding of 30 representative papers provides a quantitative foundation that exposes common assumptions—such as independent task models and simplified simulators—that limit real-world applicability. At the same time, the synthesis identifies promising trends, including reinforcement learning for adaptive decision-making and hybrid frameworks that combine convex optimization with predictive models. These insights underscore the need for future MEC strategies that balance computational efficiency, adaptability, and scalability. By synthesizing results, quantifying trends, and framing open challenges, this SLR provides researchers and practitioners with a comprehensive understanding of the strengths, limitations, and opportunities in MEC computation offloading.
Full Text:
PDFReferences
F. Richter, “Charted: There are more mobile phones than people in the world,” World Economic Forum. Accessed: May 02, 2024. [Online]. Available: https://www.weforum.org/agenda/2023/04/charted-there-are-more-phones-than-people-in-the-world/
J. Hojlo, “Future of Industry Ecosystems: Shared Data and Insights,” IDC. Accessed: May 02, 2024. [Online]. Available: https://blogs.idc.com/2021/01/06/future-of-industry-ecosystems-shared-data-and-insights/
P. Taylor, “Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts from 2021 to 2025,” Statista.
“Cloud Will Be the Centerpiece of New Digital Experiences,” Gartner. Accessed: May 05, 2024. [Online]. Available: https://www.gartner.com/en/newsroom/press-releases/2021-11-10-gartner-says-cloud-will-be-the-centerpiece-of-new-digital-experiences
M. A. E. Rasool, A. Kumar, and A. Islam, “Dynamic Task Offloading Optimization in Mobile Edge Computing Systems with Time-Varying Workloads Using Improved Particle Swarm Optimization,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 4, 2024, doi: http://dx.doi.org/10.14569/IJACSA.2024.01504122.
Y. Li and W. Zhang, “Task-Offloading Strategy of Mobile Edge Computing for WBANs,” 2024.
P. Mach and Z. Becvar, “Mobile Edge Computing: A Survey on Architecture and Computation Offloading,” IEEE Commun. Surv. Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017, doi: 10.1109/COMST.2017.2682318.
L. Lin, X. Liao, H. Jin, and P. Li, “Computation Offloading Toward Edge Computing,” Proc. IEEE, vol. 107, no. 8, 2019, doi: 10.1109/JPROC.2019.2922285.
T. Zheng, J. Wan, J. Zhang, C. Jiang, and G. Jia, “A Survey of Computation Offloading in Edge Computing,” in 2020 International Conference on Computer, Information and Telecommunication Systems (CITS), IEEE, 2020. doi: 10.1109/CITS49457.2020.9232457.
A. Shakarami, A. Shahidinejad, and M. Ghobaei-Arani, “A review on the computation offloading approaches in mobile edge computing: A game-theoretic perspective,” Softw. Pract. Exp., vol. 50, no. 9, pp. 1719–1759, 2020, doi: https://doi.org/10.1002/spe.2839.
A. Shakarami, M. Ghobaei-Arani, and A. Shahidinejad, “A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective,” Comput. Networks, vol. 182, 2020, doi: https://doi.org/10.1016/j.comnet.2020.107496.
Q.-H. Nguyen and F. Dressler, “A smartphone perspective on computation offloading—A survey,” Comput. Commun., vol. 159, pp. 133–154, 2020, doi: https://doi.org/10.1016/j.comcom.2020.05.001.
L. Zheng and L. Cai, “A Distributed Demand Response Control Strategy Using Lyapunov Optimization,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 2075–2083, 2014, doi: 10.1109/TSG.2014.2313347.
Y. Mao, J. Zhang, and K. B. Letaief, “A Lyapunov Optimization Approach for Green Cellular Networks With Hybrid Energy Supplies,” IEEE J. Sel. Areas Commun., vol. 33, no. 12, pp. 2463–2477, 2015, doi: 10.1109/JSAC.2015.2481209.
A. Ben-Tal and A. Nemirovski, LECTURES ON MODERN CONVEX OPTIMIZATION ANALYSIS, ALGORITHMS, AND ENGINEERING APPLICATIONS. Society for Industrial and Applied Mathematics Philadelphia, 2001. [Online]. Available: https://epubs.siam.org/doi/pdf/10.1137/1.9780898718829.fm
S. P. Boyd and L. Vandenberghi, Convex Optimization. Cambridge University Press, 2004.
K. Y. Lee and M. A. El-Sharkawi, Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems. WILEY INTERSCIENCE, 2008.
R. B. Myerson, Game Theory Analysis of Conflict. Harvawrd University Press, 1997.
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger, “Deep reinforcement learning that matters,” in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, AAAI Press, 2018, pp. 3207–3214. doi: 10.1609/aaai.v32i1.11694.
D. P. Bertsekas, Reinforcement Learning and Optimal Control. Athena Scientific, Belmont, MA, 2019.
P. Paymard, S. Rezvani, and N. Mokari, “Joint task scheduling and uplink/downlink radio resource allocation in PD-NOMA based mobile edge computing networks,” Phys. Commun., vol. 32, pp. 160–171, 2019, doi: https://doi.org/10.1016/j.phycom.2018.11.007.
H. Yu, Q. Wang, and S. Guo, “Energy-Efficient Task Offloading and Resource Scheduling for Mobile Edge Computing,” in 2018 IEEE International Conference on Networking, Architecture and Storage (NAS), 2018. doi: 10.1109/NAS.2018.8515731.
Y. Mao, J. Zhang, and K. B. Letaief, “Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices,” IEEE J. Sel. Areas Commun., vol. 34, no. 12, pp. 3590–3605, 2016, doi: 10.1109/JSAC.2016.2611964.
P. Liu, G. Xu, K. Yang, K. Wang, and X. Meng, “Jointly Optimized Energy-Minimal Resource Allocation in Cache-Enhanced Mobile Edge Computing Systems,” IEEE Access, vol. 7, pp. 3336–3347, 2019, doi: 10.1109/ACCESS.2018.2889815.
Y. Yang, Y. Ma, W. Xiang, X. Gu, and H. Zhao, “Joint Optimization of Energy Consumption and Packet Scheduling for Mobile Edge Computing in Cyber-Physical Networks,” IEEE Access, vol. 6, pp. 15576–15586, 2018, doi: 10.1109/ACCESS.2018.2810115.
J. Zhang, W. Xia, F. Yan, and L. Shen, “Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks with Mobile Edge Computing,” IEEE Access, vol. 6, pp. 19324–19337, 2018, doi: 10.1109/ACCESS.2018.2819690.
X. Chen, H. Zhang, C. Wu, S. Mao, and Y. Ji, “Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning,” IEEE Internet Things J., vol. 6, no. 3, pp. 4005–4018, 2019, doi: 10.1109/JIOT.2018.2876279.
Z. Tan, F. R. Yu, X. Li, H. Ji, and V. C. M. Leung, “Virtual Resource Allocation for Heterogeneous Services in Full Duplex-Enabled SCNs With Mobile Edge Computing and Caching,” IEEE Trans. Veh. Technol., vol. 67, no. 2, pp. 1794–1808, 2017, doi: 10.1109/TVT.2017.2764002.
S. Guo, J. Liu, Y. Yang, B. Xiao, and Z. Li, “Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing,” IEEE Trans. Mob. Comput., vol. 18, no. 2, pp. 319–333, 2019, doi: 10.1109/TMC.2018.2831230.
Q.-V. Pham, H. T. Nguyen, Z. Han, and W.-J. Hwang, “Coalitional Games for Computation Offloading in NOMA-Enabled Multi-Access Edge Computing,” EEE Trans. Veh. Technol., vol. 60, no. 2, pp. 1982–1993, 2020, doi: 10.1109/TVT.2019.2956224.
J. Li, H. Gao, T. Lv, and Y. Lu, “Deep reinforcement learning based computation offloading and resource allocation for MEC,” in 2018 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, 2018. doi: 10.1109/WCNC.2018.8377343.
T. D. Burd and R. W. Brodersen, “Processor design for portable systems,” J. VLSI signal Process. Syst. signal, image video Technol., vol. 13, pp. 203–221, 1996, doi: https://doi.org/10.1007/BF01130406.
Z. Song, Y. Liu, and X. Sun, “Joint Radio and Computational Resource Allocation for NOMA-Based Mobile Edge Computing in Heterogeneous Networks,” IEEE Commun. Lett., vol. 22, no. 12, pp. 2559–2562, 2018, doi: 10.1109/LCOMM.2018.2875984.
X. Long, J. Wu, and L. Chen, “Energy-Efficient Offloading in Mobile Edge Computing with Edge-Cloud Collaboration,” in International Conference on Algorithms and Architectures for Parallel Processing, 2018, pp. 460–475. doi: https://doi.org/10.1007/978-3-030-05057-3_35.
Z. Tong, X. Deng, F. Ye, S. Basodi, X. Xiao, and Y. Pan, “Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment,” Inf. Sci. (Ny)., vol. 537, pp. 116–131, 2020, doi: https://doi.org/10.1016/j.ins.2020.05.057.
Y. Cui, D. Zhang, T. Zhang, L. Chen, M. Piao, and H. Zhu, “Novel method of mobile edge computation offloading based on evolutionary game strategy for IoT devices,” AEU - Int. J. Electron. Commun., vol. 118, 2020, doi: https://doi.org/10.1016/j.aeue.2020.153134.
Z. S. C. Yi, J. Cai, “A Multi-User Mobile Computation Offloading and Transmission Scheduling Mechanism for Delay-Sensitive Applications,” IEEE Trans. Mob. Comput., vol. 19, no. 1, pp. 29–43, 2020, doi: 10.1109/TMC.2019.2891736.
Z. Zhu, J. Peng, X. Gu, H. Li, K. Liu, and Z. Zhou, “Fair Resource Allocation for System Throughput Maximization in Mobile Edge Computing,” IEEE Access, vol. 6, pp. 5332–5340, 2018, doi: 10.1109/ACCESS.2018.2790963.
J. Li, H. Gao, T. Lv, and Y. Lu, “Deep reinforcement learning based computation offloading and resource allocation for MEC,” in IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2018, 2018, pp. 1–6. doi: 10.1109/WCNC.2018.837734.
U. Rahul et al., “Dynamic service migration and workload scheduling in edge-clouds,” Perform. Eval., vol. 91, pp. 205–228, 2015, doi: 10.1016/J.PEVA.2015.06.013.
M. Guo, W. Wang, X. Huang, Y. Chen, L. Zhang, and L. Chen, “Lyapunov-Based Partial Computation Offloading for Multiple Mobile Devices Enabled by Harvested Energy in MEC,” IEEE Internet Things J., vol. 9, no. 11, pp. 9025–9038, 2022, doi: 10.1109/JIOT.2021.3108381.
K. Katsalis, T. G. Papaioannou, N. Nikaein, and L. Tassiulas, “SLA-Driven VM Scheduling in Mobile Edge Computing,” in IEEE 9th International Conference on Cloud Computing (CLOUD), San Francisco, CA, USA, 2016, pp. 750–757. doi: 10.1109/CLOUD.2016.0104.
X. Lyu, W. Ni, H. Tian, R. P. Liu, X. Wang, and G. B. Giannakis, “Optimal schedule of mobile edge computing for internet of things using partial information,” IEEE J. Sel. Areas Commun., vol. 35, no. 11, pp. 2606–2615, 2017, doi: 10.1109/JSAC.2017.2760186.
S. Xia, Z. Yao, Y. Li, and S. Mao, “Online distributed offloading and computing resource management with energy harvesting for heterogeneous mec-enabled iot,” IEEE Trans. Wirel. Commun., vol. 20, no. 10, pp. 6743–6757, 2021, doi: 10.1109/TWC.2021.3076201.
C. Li, J. Tang, and Y. Luo, “Dynamic multi-user computation offloading for wireless powered mobile edge computing,” J. Netw. Comput. Appl., vol. 131, pp. 1–15, 2019, doi: https://doi.org/10.1016/j.jnca.2019.01.020.
Y. Mao, J. Zhang, and K. B. Letaief, “Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems,” in IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, 2017. doi: 10.1109/WCNC.2017.7925615.
C. You, K. Huang, H. Chae, and B.-H. Kim, “Energy-efficient resource allocation for mobileedge computation offloading,” IEEE Trans. Wirel. Commun., vol. 16, no. 3, pp. 1397–1411, 2017, doi: 10.1109/TWC.2016.2633522.
M. Liu, F. R. Yu, Y. Teng, V. C. M. Leung, and M. Song, “Distributed resource allocation in blockchain-based video streaming systems with mobile edge computing,” IEEE Trans. Wirel. Commun., vol. 18, no. 1, pp. 695–708, 2019, doi: 10.1109/TWC.2018.2885266.
M. Salmani and T. N. Davidson, “Uplink resource allocation for multiple access computational offloading,” Signal Processing, vol. 168, 202AD, doi: https://doi.org/10.1016/j.sigpro.2019.107322.
C. Li, J. Tang, H. Tang, and Y. Luo, “Collaborative cache allocation and task scheduling for data-intensive applications in edge computing environment,” Futur. Gener. Comput. Syst., vol. 95, pp. 249–264, 2019, doi: https://doi.org/10.1016/j.future.2019.01.007.
C. Li, J. Bai, Y. Chen, and Y. Luo, “Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system,” Inf. Sci. (Ny)., vol. 516, pp. 33–55, 2020, doi: https://doi.org/10.1016/j.ins.2019.12.049.
M. Ghobaei-Arani, A. Souri, F. Safara, and M. Norouzi, “An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing,” Emerg. Telecommun. Technol., vol. 31, no. 2, 2020, doi: https://doi.org/10.1002/ett.3770.
L. Yang, C. Zhong, Q. Yang, W. Zou, and A. Fathalla, “Task offloading for directed acyclic graph applications based on edge computing in industrial internet,” Inf. Sci. (Ny)., vol. 540, pp. 51–68, 2020, doi: https://doi.org/10.1016/j.ins.2020.06.001.
W. Bai and Y. Wang, “Jointly Optimize Partial Computation Offloading and Resource Allocation in Cloud-Fog Cooperative Networks,” pp. 1–20, 2023.
D. Lv, P. Wang, Q. Wang, Y. Ding, Z. Han, and Y. Zhang, “Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System,” pp. 1–21, 2024.
J. Zhou, X. Zhang, and W. Wang, “Joint resource allocation and user association for heterogeneous services in multi-access edge computing networks,” IEEE Access, vol. 7, pp. 12272–12282, 2019, doi: 10.1109/ACCESS.2019.2892466.
S. Wang, Z. Hu, Y. Deng, and L. Hu, “Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems,” 2022.
L. Wang, W. Zhou, H. Xu, L. Li, and L. Cai, “Research on task offloading optimization strategies for vehicular networks based on game theory and deep reinforcement learning,” no. October, pp. 1–17, 2023, doi: 10.3389/fphy.2023.1292702.
K. Zhang, J. Yang, and Z. Lin, “Computation Offloading and Resource Allocation Based on Game Theory in Symmetric MEC-Enabled Vehicular Networks,” 2023.
Q. Zhang, M. Lin, L. T. Yang, Z. Chen, and P. Li, “Energy-efficient scheduling for real-time systems based on deep q-learning model,” IEEE Trans. Sustain. Comput., vol. 4, no. 1, pp. 132–141, 2019, doi: 10.1109/TSUSC.2017.2743704.
R. Garaali, C. Chaieb, W. Ajib, and M. Afif, “Learning-Based Task Offloading for Mobile Edge Computing,” in ICC 2022 - IEEE International Conference on Communications, 2022. doi: 10.1109/ICC45855.2022.9838831.
C. Silva, N. Magaia, and A. Grilo, “Task Offloading Optimization in Mobile Edge Computing based on Deep Reinforcement Learning,” in MSWiM ’23: Proceedings of the Int’l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, 2023, pp. 109–118. doi: https://doi.org/10.1145/3616388.3617539.
S. Yang, G. Lee, and L. Huang, “Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks,” pp. 1–18, 2022.
L. Meng, Y. Wang, H. Wang, X. Tong, Z. Sun, and Z. Cai, “Task offloading optimization mechanism based on deep neural network in edge ‑ cloud environment,” J. Cloud Comput., 2023, doi: 10.1186/s13677-023-00450-6.
D. Lim, W. Lee, W. T. Kim, and I. Joe, “DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing,” Sensors, vol. 22, no. 23, 2022, doi: 10.3390/s22239212.
DOI: https://doi.org/10.31449/inf.v49i21.11212
This work is licensed under a Creative Commons Attribution 3.0 License.