The Impact of RAN Slice Bandwidth Subpartitioning on Slice Performance

Kulmar, Marika; Müürsepp, Ivo; Alam, Muhammad Mahtab

doi:10.1109/iwcmc55113.2022.9825262

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…We used the same simulation methodology as our previous work [ 2 ], described in Section 2.2 , where the MATLAB tool is utilized to simulate individual subslices with specified parameters, including the number of RBs and UEs. In the first step, the slice UEs are all in one subslice, and all slice bandwidth is allocated to this one subslice.…”

Section: Slice Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

Kulmar

Müürsepp

Alam

2023

Sensors

Self Cite

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In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.

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Section: Slice Performance Evaluationmentioning

confidence: 99%

“…From our previous work [ 2 ], it is known that the slice performance depends on how many subslices it is divided into. Given that different numbers of subslices affect the slice performance, a suitable number of subslices and subslice sizes exist.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

Kulmar

Müürsepp

Alam

2023

Sensors

Self Cite

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“…Their objective is the reduction of resource usage while guaranteeing slice isolation and simultaneously accounting for each slice's average rate and delay requirements. Like the reported works, most of the other works [15][16][17][18][19] have addressed the mentioned challenges in the eld of radio slicing. In the new 5G network architectures such as Cloud-RAN and mobile MEC, the computing and storage functions are separated from the base stations and transferred to small cells, access points, microcells, CO, etc.…”

Section: Related Workmentioning

confidence: 99%

Approximate Q-learning-based (AQL) network slicing in mobile edge-cloud for delay-sensitive services

Khani,

Jamali,

Sohrabi

2023

J Supercomput

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Network slicing (NS) technology promises to provide a variety of services based on diverse latencysensitive over shared infrastructure in Mobile Edge-Cloud (MEC) by creating customized slices for each application. However, to process users' dynamic slice requests, the infrastructure provider (InP) must be the online slice acceptance check and scaled if required. Based on a business model, network revenue is dependent on the acceptance of slices and the provision of infrastructure for them. If an InP does not provide more resources for an active slice, the network is penalized and its revenues are degraded. A proper solution to the aforementioned problem is to use reinforcement learning methods. But most of these methods have challenges in continuous spaces. This work presents a reinforcement learning-based method called approximate Q-learning (AQL) to intelligently slice acceptance control (SAC) to maximize utility in MEC for latency-sensitive services. The core idea of AQL is based on Q-learning, so we have developed some of its functions to adapt to a large area of spaces and actions. We have evaluated the performance of AQL in terms of coverage, cumulative rewards, resource utilization, and revenue. The results show the proposed approach has an acceptable performance.

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“…Their goal is to reduce resource consumption while preserving slice isolation and simultaneously taking into consideration the average rate and delay requirements for each slice. The majority of the further works [15][16][17][18][19] have solved the radio slicing issues described in the reported works, as well. The processing and storage tasks are outsourced to access points, microcells and other devices in the emerging 5G network designs like Cloud-RAN and mobile MEC [20].…”

Section: Related Workmentioning

confidence: 99%

Approximate Q-Learning-based (AQL) Network Slicing in Mobile Edge-Cloud environments for Delay-sensitive Services Using Markov Decision Process

Bharanidharan¹,

Malathi²,

Sathya³

et al. 2023

Preprint

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Network slicing in mobile edge-cloud environments has gained significant attention due to its ability to provide customized network services tailored to the specific requirements of different applications. However, ensuring efficient resource allocation and meeting the tough delay requirements of delay-sensitive services remain major challenges in network slicing. In this paper, we propose an Approximate Q-Learning-based (AQL) approach for network slicing in mobile edge-cloud systems, specifically targeting delay-sensitive services. The AQL approach utilizes Q-Learning, a reinforcement learning technique, to optimize resource allocation and decision-making in network slicing. By formulating the resource allocation problem as a Markov Decision Process (MDP), the proposed AQL model learns to make intelligent decisions on resource allocation to minimize delays and maximize resource utilization. The model takes into account various factors such as network congestion, service priority, and available resources to dynamically adapt the network slice configuration. To evaluate the performance of the AQL approach, we conducted extensive simulations comparing it with traditional resource allocation methods. The results demonstrate that the AQL-based network slicing approach effectively reduces delay and improves resource utilization, outperforming conventional methods. The approach also demonstrates its adaptability to dynamic network conditions and varying service requirements. Furthermore, we investigate the impact of different factors on the AQL model's performance, such as the number of network slices, resource availability, and service priority. Our analysis provides valuable insights into the optimal configuration and operation of network slicing for delay-sensitive services. Overall, this paper contributes to the field of network slicing in mobile edge-cloud systems by introducing an AQL-based approach that efficiently addresses the resource allocation challenges for delay-sensitive services. The proposed approach offers a flexible and intelligent solution that can be applied in real-world scenarios, enabling network operators to provide reliable and high-performance network services for a variety of delay-sensitive applications.

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The Impact of RAN Slice Bandwidth Subpartitioning on Slice Performance

Cited by 4 publications

References 8 publications

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning

Approximate Q-learning-based (AQL) network slicing in mobile edge-cloud for delay-sensitive services

Approximate Q-Learning-based (AQL) Network Slicing in Mobile Edge-Cloud environments for Delay-sensitive Services Using Markov Decision Process

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