Abstract
Mobile-edge computing (MEC) has emerged as a promising paradigm that moves tasks running in the cloud to edge servers. In MEC systems, there are various individual requirements, such as less user-perceived time and lower energy consumption. In this case, substantial efforts have been paid to task allocation, aiming at enabling lower latency and higher resource utilization. However existing studies on multiple-objectives task allocation algorithms rarely consider the Pareto efficient problem, where no objective could be further improved without vitiating the other objectives’ optimization. In this paper, we propose a Pareto-efficient task-allocation framework based on a deep reinforcement learning algorithm. We give the formal formulations for objectives and construct a multi-objectives’ optimization model for task allocation. Then a Pareto efficient algorithm is proposed to solve the problem of conflicting among multi-objectives. By coordinating multi-objectives parameters get from Pareto efficient algorithm, the deep reinforcement learning model takes a Pareto-efficient task allocation to improve real-time and resource utilization performance. We evaluate the proposed framework over various real-world tasks and compare it with existing allocating tasks models in edge computing networks. By using the proposed framework, we can get an accuracy that not be lower than 90% under the 0.6 s latency requirement. The simulation results also show that the proposed framework achieves lower latency and higher resource utilization compared to other task allocation methods.
This research is supported in part by the National Science Foundation of China under Grant 62141412 and Grant 61872201, in part by the Science and Technology Development Plan of Tianjin under Grant 20JCZDJC00610, in part by the Key Research and Development Program of Guangdong under Grant 2021B0101310002, and in part by the Fundamental Research Funds for the Central Universities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
List of countries by internet connection speeds (2020). https://en.wikipedia.org/wiki/List_of_countries_by_Internet_connection_speeds, accessed May
Ahmed, A., Ahmed, E.: A survey on mobile edge computing. In: International Conference on Intelligent Systems and Control (2016)
Albers, S.: Energy-efficient algorithms. Commun. ACM 53(5), 86–96 (2010)
Dinh, T.Q., Tang, J., La, Q.D., Quek, T.Q.S.: Offloading in mobile edge computing: task allocation and computational frequency scaling. IEEE Trans. Commun. 65(8), 3571–3584 (2017). https://doi.org/10.1109/TCOMM.2017.2699660
Fan, J., Wang, Z., Xie, Y., Yang, Z.: A theoretical analysis of deep q-learning. In: Learning for Dynamics and Control, pp. 486–489. PMLR (2020)
Feng, M., Krunz, M., Zhang, W.: Joint task partitioning and user association for latency minimization in mobile edge computing networks. IEEE Trans. Veh. Technol. 70(8), 8108–8121 (2021)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the kitti dataset. Int. J. Robot. Res. (IJRR) (2013)
Geng, Y., Yang, Y., Cao, G.: Energy-efficient computation offloading for multicore-based mobile devices. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 46–54. IEEE (2018)
Lei, C.: Deep reinforcement learning. In: Lei, C. (ed.) Deep Learning and Practice with MindSpore. CIR, pp. 217–243. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-2233-5_10
Li, Y.: Deep reinforcement learning: an overview. arXiv preprint arXiv:1701.07274 (2017)
Lin, X., et al.: A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 20–28 (2019)
Liu, L., Chang, Z., Guo, X., Mao, S., Ristaniemi, T.: Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J. 5(1), 283–294 (2018). https://doi.org/10.1109/JIOT.2017.2780236
Mach, P., Becvar, Z.: MEC: a survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. (2017)
Mukherjee, M., Lei, S., Di, W.: Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun. Surv. Tutor. 20(3), 1826–1857 (2018)
Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 14–23 (2009)
Sun, Y., Lin, F., Xu, H.: Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II. Wireless Pers. Commun. 102(2), 1369–1385 (2018)
Sun, Z., Liu, Y., Tao, L.: Attack localization task allocation in wireless sensor networks based on multi-objective binary particle swarm optimization. J. Netw. Comput. Appl. 112, 29–40 (2018). https://doi.org/10.1016/j.jnca.2018.03.023. https://www.sciencedirect.com/science/article/pii/S1084804518301103
Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820–2828 (2019)
Tolmidis, A.T., Petrou, L.: Multi-objective optimization for dynamic task allocation in a multi-robot system. Eng. Appl. Artif. Intell. 26(5), 1458–1468 (2013). https://doi.org/10.1016/j.engappai.2013.03.001. https://www.sciencedirect.com/science/article/pii/S0952197613000377
Wang, F., Xu, J., Wang, X., Cui, S.: Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans. Wireless Commun. 17(3), 1784–1797 (2017)
Wang, L., Jiao, L., Li, J., Mühlhäuser, M.: Online resource allocation for arbitrary user mobility in distributed edge clouds. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 1281–1290. IEEE (2017)
Wang, S., Zhao, Y., Huang, L., Xu, J., Hsu, C.H.: QoS prediction for service recommendations in mobile edge computing. J. Parallel Distrib. Comput. 127, 134–144 (2019)
Wang, S., Li, J., Wu, G., Chen, H., Sun, S.: Joint optimization of task offloading and resource allocation based on differential privacy in vehicular edge computing. IEEE Trans. Comput. Soc. Syst. 9(1), 109–119 (2021)
Xu, Z., et al.: Experience-driven networking: a deep reinforcement learning based approach. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 1871–1879. IEEE (2018)
Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced CPU energy. In: Proceedings of IEEE 36th Annual Foundations of Computer Science, pp. 374–382. IEEE (1995)
Yi, S., Qin, Z., Li, Q.: Security and privacy issues of fog computing: a survey. In: Xu, K., Zhu, H. (eds.) WASA 2015. LNCS, vol. 9204, pp. 685–695. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21837-3_67
Zhang, F., et al.: A load-aware resource allocation and task scheduling for the emerging cloudlet system. Futur. Gener. Comput. Syst. 87, 438–456 (2018)
Zhang, P., Wang, Y., Kumar, N., Jiang, C., Shi, G.: A security and privacy-preserving approach based on data disturbance for collaborative edge computing in social IoT systems. IEEE Trans. Comput. Soc. Syst. 9(1), 97–108 (2021)
Zhou, J., Zhao, X., Zhang, X., Zhao, D., Li, H.: Task allocation for multi-agent systems based on distributed many-objective evolutionary algorithm and greedy algorithm. IEEE Access 8, 19306–19318 (2020)
Zhou, S., Jadoon, W.: Jointly optimizing offloading decision and bandwidth allocation with energy constraint in mobile edge computing environment. Computing 1–27 (2021)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Liu, W., Zhao, S., Yu, Z., Wang, G., Liu, X. (2022). A Pareto-Efficient Task-Allocation Framework Based on Deep Reinforcement Learning Algorithm in MEC. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 461. Springer, Cham. https://doi.org/10.1007/978-3-031-24386-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-24386-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24385-1
Online ISBN: 978-3-031-24386-8
eBook Packages: Computer ScienceComputer Science (R0)