Abstract
To meet the ever-increasing service quality requirements of end-users and enable delay-sensitive applications to be completed within a tolerable time, Mobile Edge Computing (MEC) offloads the request of users to the edge servers that are closer to the end equipment. However, deploying a single service replication in an appropriate edge node is difficult to deal with all requests of users for multiple services. In addition, after the service replication is deployed at the edge node, a corresponding user request offloading scheme is also required. Considering the heterogeneity of edge servers, this paper studies the joint optimization problem of multi-service replication and request offloading. Firstly, we present an edge computing architecture with multi-service replication, and define the multi-service replication and request offloading as a joint optimization problem. Secondly, a multi-service replication algorithm called Multireplicas Greedy Best (MGB) is proposed to solve the joint optimization problem. Finally, the simulation experiments are carried out. The experimental results show that the proposed algorithm can effectively reduce the overall delay compared with the random strategy, the nearest node offloading strategy, the particle swarm algorithm, and the greedy algorithm.
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References
Hu, S., Li, G.: Fault-tolerant clustering topology evolution mechanism of wireless sensor networks. IEEE Access 6, 28085–28096 (2018)
Chen, M., Wang, T., Ota, K., Dong, M., Liu, A.: Intelligent resource allocation management for vehicles network: an A3C learning approach. Comput. Commun. 151, 485–494 (2020)
Cisco Systems. https://www.cisco.com/c/zh_cn.html. Accessed 8 Aug 2021
Wang, T., Cao, Z., Wang, S., et al.: Privacy-enhanced data collection based on deep learning for Internet of vehicles. IEEE Trans. Industr. Inf. 16(10), 6663–6672 (2019)
Wang, T., Jia, W., Xing, G., et al.: Exploiting statistical mobility models for efficient Wi-Fi deployment. IEEE Trans. Veh. Technol. 62(1), 360–373 (2012)
Moubayed, A., Shami, A., Heidari, P., Larabi, A., Brunner, R.: Edge-enabled V2X service placement for intelligent transportation systems. IEEE Trans. Mob. Comput. 20(4), 1380–1392 (2021)
Thai, M., Lin, Y., Lai, Y., Chien, H.: Workload and capacity optimization for cloud-edge computing systems with vertical and horizontal offloading. IEEE Trans. Netw. Serv. Manage. 17(1), 227–238 (2020)
Naas, M.I., Parvedy, P.R., Boukhobza, J., Lemarchand, L.: iFogStor: an IoT data placement strategy for fog infrastructure. In: Fog and Edge Computing (ICFEC), pp. 97–104 (2017)
Liu, X., Yu, J., Feng, Z., Gao, Y.: Multi-agent reinforcement learning for resource allocation in IoT networks with edge computing. China Commun. 17(9), 220–236 (2020)
Yu, X., Tang, L.: Competition and cooperation between edge and remote clouds: a Stackelberg game approach. In: IEEE 4th International Conference on Computer and Communications (ICCC), pp. 1919–1923 (2018)
Wang, Y., Sheng, M., Wang, X., Wang, L., Li, J.: Mobile-edge computing: partial computation offloading using dynamic voltage scaling. IEEE Trans. Commun. 64(10), 4268–4282 (2016)
Meye, P., Raipin, P., Tronel, F., Anceaume, E.: Toward a distributed storage system leveraging the DSL infrastructure of an ISP. In: 11th Consumer Communications and Networking Conference (CCNC), pp. 533–534 (2014)
Chang, W., Wang, P.: An adaptable replication scheme in mobile online system for mobile-edge cloud computing. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 109–114 (2017)
Kiani, A., Ansari, N., Khreishah, A.: Hierarchical capacity provisioning for fog computing. IEEE Trans. Netw. 27(3), 962–971 (2019)
Lin, B., et al.: A time-driven data placement strategy for a scientific workflow combining edge computing and cloud computing. IEEE Trans. Industr. Inf. 15(7), 4254–4265 (2019)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 62072216).
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Li, C., Li, G., Hu, S., Dai, C., Li, D. (2022). Joint Optimization Scheme of Multi-service Replication and Request Offloading in Mobile Edge Computing. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_28
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DOI: https://doi.org/10.1007/978-3-030-95384-3_28
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