User Request Provisioning Oriented Slice Anomaly Prediction and Resource Allocation in 6G Networks
Ming, Zhao; Yu, Hao; Taleb, Tarik (2024-08-20)
IEEE
20.08.2024
Z. Ming, H. Yu and T. Taleb, "User Request Provisioning Oriented Slice Anomaly Prediction and Resource Allocation in 6G Networks," ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 3640-3645, doi: 10.1109/ICC51166.2024.10622281
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© 2024 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-202410026147
https://urn.fi/URN:NBN:fi:oulu-202410026147
Tiivistelmä
Abstract
Satisfying users' requests based on the service level agreements of network slices is one of the most basic and vital topics of network slicing in 6G networks, and anomaly detection is regarded as a key technique for locating the abnormal status of slices. However, current studies on slice anomaly detection mostly focused on real-time monitoring of slices and ignored the prediction of potential anomalies. Generally, when anomalies trigger, it is hard for slices to adjust the resources in time due to resource competition among physical/virtual nodes. Besides, the resource provisioning strategies can also be optimized when slices are running normally, which is seldom considered when performing slice anomaly detection. To cope with these challenges, in this paper, we are motivated to locate the potential slice anomalies and optimize the resource allocation strategies in a holistic view by learning users' historical behaviors. Specifically, we design a general network architecture, model the process of slice resource provisioning, and formulate the problem as maximizing the long-term system net promoter score (NPS). To solve this problem, we propose a framework to locate the potential slice anomalies and decide the resource allocation strategies simultaneously by predicting the users' future requests and positions. As a result, simulation results demonstrate that our proposed scheme outperforms other baselines in improving the long-term system NPS and reducing the average latency of users.
Satisfying users' requests based on the service level agreements of network slices is one of the most basic and vital topics of network slicing in 6G networks, and anomaly detection is regarded as a key technique for locating the abnormal status of slices. However, current studies on slice anomaly detection mostly focused on real-time monitoring of slices and ignored the prediction of potential anomalies. Generally, when anomalies trigger, it is hard for slices to adjust the resources in time due to resource competition among physical/virtual nodes. Besides, the resource provisioning strategies can also be optimized when slices are running normally, which is seldom considered when performing slice anomaly detection. To cope with these challenges, in this paper, we are motivated to locate the potential slice anomalies and optimize the resource allocation strategies in a holistic view by learning users' historical behaviors. Specifically, we design a general network architecture, model the process of slice resource provisioning, and formulate the problem as maximizing the long-term system net promoter score (NPS). To solve this problem, we propose a framework to locate the potential slice anomalies and decide the resource allocation strategies simultaneously by predicting the users' future requests and positions. As a result, simulation results demonstrate that our proposed scheme outperforms other baselines in improving the long-term system NPS and reducing the average latency of users.
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