Abstract
In heterogeneous edge computing, multiple tasks often compete for limited computing resources on the same edge server. These tasks request different edge computing services and usually have a deadline. Efficiently scheduling them is a complex and challenging problem. In this paper, we first develop a model for grouping and mapping limited edge computing resources. Then, we mathematically describe the multi-task scheduling problem with deadline constraints. Third, we propose a grouping-based multi-task scheduling strategy called GMTSS, which includes task regrouping and priority sorting, a resource-aware greedy scheduling algorithm, and a task adjusting method. Task regrouping and priority sorting are designed to balance the efficiency and fairness of scheduling multiple tasks. The greedy scheduling algorithm assigns tasks to an optimal node based on the status of resource groups. Additionally, task adjusting aims to achieve a better scheduling scheme that will meet the maximum number of deadlines or higher long-term satisfaction of system service, called LTSS. We conduct large-scale simulations, and the experimental results clearly show that our proposed GMTSS outperforms the current state-of-the-art benchmark strategy in terms of task completion rate within deadlines and LTSS. Furthermore, GMTSS performs well in terms of task completion time.
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References
Abadi, Z.J.K., Mansouri, N., Khalouie, M.: Task scheduling in fog environment-challenges, tools & methodologies: a review. Comput. Sci. Rev. 48, 100550 (2023)
Bellendorf, J., Mann, Z.Á.: Classification of optimization problems in fog computing. Futur. Gener. Comput. Syst. 107, 158–176 (2020)
Fang, J., Zhang, J., Lu, S., Zhao, H., Zhang, D., Cui, Y.: Task scheduling strategy for heterogeneous multicore systems. IEEE Consumer Electron. Mag. 11(1), 73–79 (2021)
Feng, A., Dong, D., Lei, F., Ma, J., Yu, E., Wang, R.: In-network aggregation for data center networks: a survey. Comput. Commun. 198, 63–76 (2023)
Filali, A., Abouaomar, A., Cherkaoui, S., Kobbane, A., Guizani, M.: Multi-access edge computing: a survey. IEEE Access 8, 197017–197046 (2020)
Han, Z., Tan, H., Li, X.Y., Jiang, S.H.C., Li, Y., Lau, F.C.: Ondisc: online latency-sensitive job dispatching and scheduling in heterogeneous edge-clouds. IEEE/ACM Trans. Netw. 27(6), 2472–2485 (2019)
Hong, C.H., Varghese, B.: Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comput. Surv. (CSUR) 52(5), 1–37 (2019)
Jagadish, T., Apte, O., Pradeep, K.: Task scheduling algorithms in fog computing: a comparison and analysis. In: 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), pp. 483–488. IEEE (2022)
Li, J., et al.: Maximizing user service satisfaction for delay-sensitive Iot applications in edge computing. IEEE Trans. Parallel Distrib. Syst. 33(5), 1199–1212 (2021)
Luo, Q., Hu, S., Li, C., Li, G., Shi, W.: Resource scheduling in edge computing: a survey. IEEE Commun. Surv. Tutorials 23(4), 2131–2165 (2021)
Meng, J., Tan, H., Li, X.Y., Han, Z., Li, B.: Online deadline-aware task dispatching and scheduling in edge computing. IEEE Trans. Parallel Distrib. Syst. 31(6), 1270–1286 (2019)
Oo, T., Ko, Y.B.: Application-aware task scheduling in heterogeneous edge cloud. In: 2019 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1316–1320. IEEE (2019)
Tang, X., et al.: Cost-efficient workflow scheduling algorithm for applications with deadline constraint on heterogeneous clouds. IEEE Trans. Parallel Distrib. Syst. 33(9), 2079–2092 (2022)
Xu, B., et al.: Fine-grained task scheduling based on priority for heterogeneous mobile edge computing. In: 2022 China Automation Congress (CAC), pp. 4889–4894. IEEE (2022)
Yu, W., et al.: A survey on the edge computing for the internet of things. IEEE access 6, 6900–6919 (2017)
Yuan, H., Tang, G., Li, X., Guo, D., Luo, L., Luo, X.: Online dispatching and fair scheduling of edge computing tasks: a learning-based approach. IEEE Internet Things J. 8(19), 14985–14998 (2021)
Yuchong, L., Jigang, W., Yalan, W., Long, C.: Task scheduling in mobile edge computing with stochastic requests and m/m/1 servers. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 2379–2382. IEEE (2019)
Zhu, T., Shi, T., Li, J., Cai, Z., Zhou, X.: Task scheduling in deadline-aware mobile edge computing systems. IEEE Internet Things J. 6(3), 4854–4866 (2018)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61972146, 62002032, 62372064), the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20220942).
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Tang, X., Cao, W., Deng, T., Xu, C., Zhu, Z. (2024). A Grouping-Based Multi-task Scheduling Strategy with Deadline Constraint on Heterogeneous Edge Computing. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14488. Springer, Singapore. https://doi.org/10.1007/978-981-97-0801-7_27
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DOI: https://doi.org/10.1007/978-981-97-0801-7_27
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