Abstract
Mobile edge computing (MEC) deploys the edge computing servers (ECSs) to the network edge and alleviates the problems of limited computational resources and power for mobile users’ equipment (UE). Thus, MEC supports the massive computation-intensive applications in the integrated communications and computing 6G network (CCN). However, in mobility management of CCN, users’ mobility triggers the new handover between not only two BSs but also two ECSs. When we select the optimal BS, we also need to consider whether the co-located ECS has the sufficient computational resources and low queuing delay. To obtain the lower offloading delay, the existing offloading methods produce extra handover in the decision of the optimal ECS. What’s more, the existing handover decision methods ignore the problem of limited computational resources of ECS. In this paper, to meet the demands of communication and computation services, we propose a joint decision method based on multi-objective optimization method (JD-MOO) to solve the joint handover decision and computation offloading problem. We define the services satisfaction degree functions to evaluate the quality of two services. Simulation results show that the proposed JD-MOO method has good performance of handover and offloading.
Supported by the National Natural Science Foundation of China (No. 61772385).
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Wu, DF., Huang, C., Yin, Y., Huang, S., Gong, H. (2024). Multi-objective Optimization for Joint Handover Decision and Computation Offloading in Integrated Communications and Computing 6G Networks. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14490. Springer, Singapore. https://doi.org/10.1007/978-981-97-0859-8_11
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