Network Slice Mobility for 6G Networks by Exploiting User and Network Prediction
Yu, Hao; Ming, Zhao; Wang, Chenyang; Taleb, Tarik (2023-10-23)
IEEE
23.10.2023
H. Yu, Z. Ming, C. Wang and T. Taleb, "Network Slice Mobility for 6G Networks by Exploiting User and Network Prediction," ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 4905-4911, doi: 10.1109/ICC45041.2023.10279739.
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202312123667
https://urn.fi/URN:NBN:fi:oulu-202312123667
Tiivistelmä
Abstract
Beyond 5G applications, future 6G services would need to support very large data volumes for emerging industry verticals, such as holographic-type communications, as well as time-sensitive services, e.g., industrial control. Network slicing is the key technology to deliver such customizable services. Slices and their dedicated resources should be provisioned optimally where the services will be run with low network latencies and associated expenses. However, the user dynamics on resource demands within and between slices result in different resource re-allocation triggers, ultimately lead to distinct mobility patterns, e.g., scaling, migration, where sufficient resources must be transferred. Efficient slice mobility requires increasing flexibility in network operation and management to ensure the customized QoS while minimizing the corresponding mobility cost. In this paper, a prediction-based intelligent network analytic is proposed to facilitate the optimized network slice mobility scheme. We will investigate how to utilize the user and network prediction as the auxiliary information to make the slice mobility decision with the objective of maximizing the long-term profits while minimizing the latency and mobility cost. Finally, we evaluate the proposed prediction-based network slice mobility scheme in a simulated environment and compare its performance in terms of system costs, revenues, and profits with two benchmark solutions.
Beyond 5G applications, future 6G services would need to support very large data volumes for emerging industry verticals, such as holographic-type communications, as well as time-sensitive services, e.g., industrial control. Network slicing is the key technology to deliver such customizable services. Slices and their dedicated resources should be provisioned optimally where the services will be run with low network latencies and associated expenses. However, the user dynamics on resource demands within and between slices result in different resource re-allocation triggers, ultimately lead to distinct mobility patterns, e.g., scaling, migration, where sufficient resources must be transferred. Efficient slice mobility requires increasing flexibility in network operation and management to ensure the customized QoS while minimizing the corresponding mobility cost. In this paper, a prediction-based intelligent network analytic is proposed to facilitate the optimized network slice mobility scheme. We will investigate how to utilize the user and network prediction as the auxiliary information to make the slice mobility decision with the objective of maximizing the long-term profits while minimizing the latency and mobility cost. Finally, we evaluate the proposed prediction-based network slice mobility scheme in a simulated environment and compare its performance in terms of system costs, revenues, and profits with two benchmark solutions.
Kokoelmat
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