Abstract
Edge computing enhances the processing capabilities of edge networks for processing mobile users’ jobs. Approaches that dispatch jobs to a single edge cloud are prone to cause task accumulation and excessive latency due to the uncertain workload and limited resources of edge servers. Offloading tasks to lightly-loaded neighbors, which are multiple hops away, alleviates the dilemma but increases transmission cost and security risks. Hence, how to realize the trade-off between computing latency, offloading cost and security during job dispatching is a great challenge. In this paper, we propose an online Deep learning-based model for Secure Collaborative Job Dispatching (DeepSCJD) in multiple edge clouds. Specifically, we first utilize bi-directional long short-term memory to predict the workload of edge servers and apply the graph neural networks to aggregate the features of directed acyclic graph jobs as well as undirected weighted topology of edge servers. Based on the state composed of these two features, a deep reinforcement learning agent including a simple deep Q network and linear branch, generates a final dispatching decision of tasks, aiming to achieve the smallest average weighted cost. Experiments on real-world data sets demonstrate the efficiency of proposed model and its superiority over traditional and state-of-the-art baselines, reaching the maximum average performance improvement of 54.16% relative to K-Hop. Extensive evaluations manifest the generalization of our model under various conditions.
This research is supported in part by the NSF of China (No. 62141412, 61872201), the Science and Technology Development Plan of Tianjin (20JCZDJC00610, 19YFZCSF00900, the Fundamental Research Funds for the Central Universities, China Scholarship Council (CSC). The first author is supported by CSC (Grant No.202106200061) as a visiting Ph.D. student at the University of British Columbia, Canada under the supervision of Prof. Victor C.M. Leung.
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Small Cell Market Status Statistics Dec 2017. scf.io. Retrieved 2018-02-19.
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References
Bi, S., Zhang, Y.J.: Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading. IEEE Trans. Wirel. Commun. 17(6), 4177–4190 (2018)
Chen, J., Yang, Y., Wang, C., Zhang, H., Qiu, C., Wang, X.: Multi-task offloading strategy optimization based on directed acyclic graphs for edge computing. IEEE Internet of Things J. 7, 1678–1689 (2021)
Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2015)
Eshraghi, N., Liang, B.: Joint offloading decision and resource allocation with uncertain task computing requirement. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 1414–1422. IEEE (2019)
Guan, W., Zhang, H., Leung, V.C.: Customized slicing for 6g: enforcing artificial intelligence on resource management. IEEE Netw. 35(5), 264–271 (2021)
He, X., Lu, H., Huang, H., Mao, Y., Wang, K., Guo, S.: QOE-based cooperative task offloading with deep reinforcement learning in mobile edge networks. IEEE Wirel. Commun. 27(3), 111–117 (2020)
Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)
Huang, L., Bi, S., Zhang, Y.J.A.: Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks. IEEE Trans. Mob. Comput. 19(11), 2581–2593 (2019)
Kelly, R.: Internet of things data (2015). https://campustechnology.com/articles/2015/04/15/internet-of-things-data-to-top-1-6-zettabytes-by-2020.aspxd
Lei, L., Xu, H., Xiong, X., Zheng, K., Xiang, W.: Joint computation offloading and multiuser scheduling using approximate dynamic programming in NB-IOT edge computing system. IEEE Internet Things J. 6(3), 5345–5362 (2019)
Li, Y., Wang, X., Gan, X., Jin, H., Fu, L., Wang, X.: Learning-aided computation offloading for trusted collaborative mobile edge computing. IEEE Trans. Mob. Comput. 19(12), 2833–2849 (2019)
Liu, L., Tan, H., Jiang, S.H.C., Han, Z., Li, X.Y., Huang, H.: Dependent task placement and scheduling with function configuration in edge computing. In: 2019 IEEE/ACM 27th International Symposium on Quality of Service (IWQoS), pp. 1–10. IEEE (2019)
Liu, Y., Yu, H., Xie, S., Zhang, Y.: Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks. IEEE Trans. Veh. Technol. 68(11), 11158–11168 (2019)
Pu, L., Chen, X., Xu, J., Fu, X.: D2D fogging: an energy-efficient and incentive-aware task offloading framework via network-assisted D2D collaboration. IEEE J. Sel. Areas Commun. 34(12), 3887–3901 (2016)
Saleem, U., Liu, Y., Jangsher, S., Li, Y.: Performance guaranteed partial offloading for mobile edge computing. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2018)
Saleem, U., Liu, Y., Jangsher, S., Li, Y., Jiang, T.: Mobility-aware joint task scheduling and resource allocation for cooperative mobile edge computing. IEEE Trans. Wirel. Commun. 20(1), 360–374 (2020)
Sun, M., Bao, T., Xie, D.: Towards application-driven task offloading in edge computing based on deep reinforcement learning. Micromachines 12(9), 1011 (2021)
Tang, Z., Lou, J., Zhang, F., Jia, W.: Dependent task offloading for multiple jobs in edge computing. In: 2020 29th International Conference on Computer Communications and Networks (ICCCN), pp. 1–9. IEEE (2020)
Xu, Z., et al.: Learning for exception: Dynamic service caching in 5g-enabled MECS with bursty user demands. In: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), pp. 1079–1089. IEEE (2020)
Yu, Z., Liu, W., Liu, X., Wang, G.: Drag-JDEC: a deep reinforcement learning and graph neural network-based job dispatching model in edge computing. In: 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), pp. 1–10. IEEE (2021)
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Yu, Z. et al. (2022). DeepSCJD: An Online Deep Learning-Based Model for Secure Collaborative Job Dispatching in Edge Computing. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernández, P., Ruiz-Cortés, A. (eds) Service-Oriented Computing. ICSOC 2022. Lecture Notes in Computer Science, vol 13740. Springer, Cham. https://doi.org/10.1007/978-3-031-20984-0_34
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