Abstract
In this paper, we study the joint optimization problem of computation path selection and workload allocation for in-network computing at the edge. The existing works, which only consider the end-to-end latency, ignore the operational cost of the servers and the dynamic tasks arrived in an online manner. Thus, we investigate the first online scheduling problem of path selection and workload allocation for in-network computing. Such a problem is modeled as a mixed integer programming problem, which tries to jointly minimize the server operating cost and end-to-end latency. Then, a dynamic 3D-map is constructed to take both the operation cost and end-to-end latency into account. Based on the constructed 3D-map, a cooperative learning based algorithm with ant colony optimization is proposed. Finally, the extensive simulation demonstrates the proposed algorithm shows good robustness and outperforms the state-of-the-art algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, R., Xie, Z., Yu, D., et al.: Digital twin-assisted federated learning service provisioning over mobile edge networks. IEEE Trans. Comput. 73(2), 586–598 (2024)
Cai, Z., Chen, Q., Shi, T., et al.: Battery-free wireless sensor networks: a comprehensive survey. IEEE Internet Things J. 10(6), 5543–5570 (2023)
Liu, Z., Li, F., Yu, D., et al.: Online learning-based allocation of base stations and channels in cognitive radio networks. In: Proceedings of the WASA, pp. 346–358 (2020)
Zhou, X., et al.: Hierarchical federated learning with social context clustering-based participant selection for internet of medical things applications. IEEE Trans. Comput. Soc. Syst. 10(4), 1742–1751 (2023)
Chen, Q., et al.: Low latency broadcast scheduling for battery-free wireless networks without predetermined structures. In: Proceedings of the ICDCS, pp. 245–255 (2020)
Li, J., Guo, S., Liang, W., et al.: Digital twin-enabled service provisioning in edge computing via continual learning. IEEE Trans. Mob. Comput. (2023)
Guo, X., Dong, F., Shen, D., et al.: Exploiting the computational path diversity with in-network computing for MEC. In: Proceedings of the IEEE SECON, pp. 280–288 (2022)
Zheng, C., Tang, H., Zang, M., et al.: DINC: toward distributed in-network computing. In: Proceedings of the ACM CoNEXT (2023)
Liu, B., Cao, Y., Zhang, Y., Jiang, T.: A distributed framework for task offloading in edge computing networks of arbitrary topology. IEEE Trans. Wirel. Commun. 19(4), 2855–2867 (2020)
Jin, P., Fei, X., Zhang, Q., et al.: Latency-aware VNF chain deployment with efficient resource reuse at network edge. In: Proceedings of the IEEE INFOCOM (2020)
Ren, H., Xu, Z., Liang, W., et al.: Efficient algorithms for delay-aware NFV-enabled multicasting in mobile edge clouds with resource sharing. IEEE Trans. Parallel Distrib. Syst. 31(9), 2050–2066 (2020)
Agarwal, S., Malandrino, F., Chiasserini, C.F., et al.: Joint VNF placement and CPU allocation in 5G. In: Proceedings of the IEEE INFOCOM, Honolulu, HI, USA (2018)
Zheng, D., Peng, C., Liao, X., et al.: Towards latency optimization in hybrid service function chain composition and embedding. In: Proceedings of the IEEE INFOCOM, Toronto, ON, Canada, 6-9 July 2020, pp. 1539–1548 (2020)
Hung, Y.W., Chen, Y.C., Lo, C., et al.: Dynamic workload allocation for edge computing. IEEE Trans. VLSI Syst. 29(3), 519–529 (2021)
Li, D., Hong, P., Xue, K., Pei, J.: Virtual network function placement considering resource optimization and SFC requests in cloud datacenter. IEEE Trans. Parallel Distrib. Syst. 29(7), 1664–1677 (2018)
Misra, S., Saha, N.: Detour: dynamic task offloading in software-defined fog for IoT applications. IEEE J. Sel. Areas Commun. 37(5), 1159–1166 (2019)
Chen, Q. Guo, S., Wang, K., et al.: Towards real-time inference offloading with distributed edge computing: the framework and algorithms. IEEE Trans. Mob. Comput., 1–18 (2023)
Li, J., et al.: SFC-enabled reliable service provisioning in mobile edge computing via digital twins. In: Proceedings of the IEEE MASS, pp. 311–317 (2022)
Li, J., Guo, S., Liang, W., et al.: Digital twin-assisted, SFC-enabled service provisioning in mobile edge computing. IEEE Trans. Mob. Comput. 23(1), 393–408 (2024)
Meng, X., Wang, W., et al.: Closed-form delay-optimal computation offloading in mobile edge computing systems. IEEE Trans. Wirel. Commun. (2019)
Yang, C.S., et al.: Communication-aware scheduling of serial tasks for dispersed computing. IEEE/ACM Trans. Netw. 27(4), 1330–1343 (2019)
Liu, J., Xu, H., Zhao, G., et al.: Incremental server deployment for scalable NFV-enabled networks. In: Proceedings of the IEEE INFOCOM, pp. 2361–2370 (2020)
Shang, X., Huang, Y., Liu, Z., Yang, Y.: Reducing the service function chain backup cost over the edge and cloud by a self-adapting scheme. IEEE Trans. Mob. Comput. 21(8), 2994–3008 (2022)
Cziva, R., Anagnostopoulos, C., Pezaros, D.P.: Dynamic, latency-optimal vNF placement at the network edge. In: Proceedings of the IEEE INFOCOM, Honolulu, HI (2018)
Mao, Y., Shang, X., Yang, Y.: Provably efficient algorithms for traffic-sensitive SFC placement and flow routing. In: Proceedings of the IEEE INFOCOM (2022)
Mao, Y., Shang, X., Yang, Y.: Ant colony based online learning algorithm for service function chain deployment. In: Proceedings of the IEEE INFOCOM (2023)
Cai, Z., et al.: Latency-and-coverage aware data aggregation scheduling for multihop battery-free wireless networks. IEEE Trans. Wirel. Commun. 20(3), 1770–1784 (2021)
Chen, Q., et al.: Energy-collision aware minimum latency aggregation scheduling for energy-harvesting sensor networks. ACM Trans. Sens. Netw. 17(4), 1–34 (2021)
Zhou, X., Zheng, X., Cui, X., et al.: Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mobile networks. IEEE J. Sel. Areas Commun. 41(10), 3191–3211 (2023)
Chen, Q., Gao, H., Li, Y., et al.: Edge-based beaconing schedule in duty-cycled multihop wireless networks. In: Proceedings of INFOCOM (2017)
Li, L., et al.: Towards efficient and delay-aware NFV-enabled unicasting with adjustable service function chains. IEEE Open J. Comput. Soc. (2022)
Zhang, J., et al.: Task-oriented energy scheduling in wireless rechargeable sensor networks. ACM Trans. Sen. Netw. 19(4), 1–32 (2023)
Dorigo, M., et al.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evolut. Comput. 1(1), 53–66 (1997)
Acknowledgment
This work was supported by the NSFC under Grant No. 62372118, the Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515030136), the Guangzhou Science and Technology Plan under Grant 2023A04J1701.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ouyang, S., Zhang, F., Mai, J., Chai, Y., Chen, Q., Tao, Y. (2025). Efficient Online Path Selection and Workload Allocation for In-Network Computing in MEC. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14999. Springer, Cham. https://doi.org/10.1007/978-3-031-71470-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-71470-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-71469-6
Online ISBN: 978-3-031-71470-2
eBook Packages: Computer ScienceComputer Science (R0)