Reinforcement Learning Based Resource Management for Network Slicing

Kim, Young S.; Kim, Sunyong; Lim, Hyuk

doi:10.3390/app9112361

Cited by 41 publications

(33 citation statements)

References 15 publications

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“…In recent studies, the RL algorithms have been applied successfully as an alternative to model-based optimization, which is difficult to handle because of dynamics complexity involving slice requests and resource allocation [6], [7], [23]- [25]. In [2], we focused on the dynamic resource allocation of network slicing against upcoming resource demand by applying Qlearning. The study aims at maximizing the profit of slices while ensuring the QoS requirements of end-users.…”

Section: Related Workmentioning

confidence: 99%

“…Each network slice can have its own network architecture, including on-demand service application, resource capacity, and control policy, to balance the disparate requirements between heterogeneous services. For example, The dynamic resource allocation framework of network slicing can support real-time service requirements by considering service characteristics and time-varying demands of different slices [2]. While this vertical network slicing originally focused on core networks, it has expanded over time to end-to-end network slicing, supporting radio access networks and wireless interfaces [3].…”

Section: Introductionmentioning

confidence: 99%

“…Reinforcement learning (RL) has been applied to network slicing management problems for 5G networks in [2], [6], [7]. RL is a type of machine learning technique that enables an agent to learn an optimal policy through interaction with an environment.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

Kim

Lim

2021

IEEE Access

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To meet the explosive growth of mobile traffic, the 5G network is designed to be flexible and support multi-access edge computing (MEC), thereby improving the end-to-end quality of service (QoS). In particular, 5G network slicing, which allows a physical infrastructure to split into multiple logical networks, keeps the balance of network resource allocation among different service types with on-demand resource requests. However, achieving effective resource allocation across the end-to-end network is difficult due to the dynamic characteristics of slicing requests such as uncertain real-time resource demand and heterogeneous requirements. In this paper, we develop a reinforcement learning (RL)-based dynamic resource allocation framework for end-to-end network slicing with heterogeneous requirements in multi-layer MEC environments. We first design a hierarchical MEC architecture and formulate a resource allocation problem for the end-to-end network slicing as an optimization problem using the Markov decision process (MDP). Using proximal policy optimization (PPO), we develop independently-collaborative and jointlycollaborative dynamic resource allocation algorithms to maximize resource efficiency while satisfying the QoS of slices. Experimental results show that the proposed algorithms can recognize the characteristics of slice requests and coming resource demands and efficiently allocate resources with a high QoS satisfaction rate.INDEX TERMS 5G, network slicing, multi-access edge computing, network resource management, multiagent reinforcement learning.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

Kim

Lim

2021

IEEE Access

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“…Two types of services have been considered in [58] to improve the networks' flexibility. By focusing on the implementation of the RAN in the cloud by using the SCA problem but in [59] authors tackled the problem by using machine learning as in [60], and SNR in [59], but not with the 5G entities. While Markov Decision process was applied for resources allocation in RAN [61] to enhance the flexibility of the 5G systems.…”

Section: F Resources and Policy Managementmentioning

confidence: 99%

A Survey of Mobility Management as a Service in Real-Time Inter/Intra Slice Control

et al. 2021

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In-network softwarization, Network Slicing provides scalability and flexibility through various services such as Quality of Service (QoS) and Quality of Experience (QoE) to cover the network demands. For the QoS, a set of policies must be considered in real-time, accompanied by a group of functions and services to guarantee the end-user needs based on network demand. On the other hand, for the QoE, the service's performance needs to be improved to bring an efficient service to cover the demands of the end-user. The 3G Partnership Project (3GPP) defined the slice as a component of resources used to process a set of packets. These resources need to be flexible, which means the resources can be scaled up or down based on the demand. This survey discusses softwarization and virtualization techniques, considering how to implement the slices for future networks. Specifically, we discuss current advances concerning the functionality and architecture of the 5G network. Therefore, the paper critically evaluates recent research and systems related to mobility management as a service in real-time inter/intra slice control by considering the strengths and limitations of these contributions to identify the research gaps and possible research directions for emerging research and development opportunities. Moreover, we extend our review by considering the slice types and their numbers based on the 3GPP Technical Specification (3GPP TS). The study presented in this paper identifies open issues and research directions that reveal that mobility management at a service level with inter/intra slice management techniques has strong potential in future networks and requires further investigation from the research community to exploit its benefits fully.

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“…The slice provisioning system interacts with the three roles to orchestrate and manage physical resources. Although network slicing has attracted increasing attention from both academia and industry [13], slice provisioning is a key issue to be addressed [14]. Slice provisioning is an approach to creating separate virtual networks based on service requirements using common physical computing and networking resources.…”

Section: Introductionmentioning

confidence: 99%

Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes

Guo²,

Xu³

et al. 2019

Applied Sciences

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Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the k-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called minMaxBWUtilHops, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio.

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Reinforcement Learning Based Resource Management for Network Slicing

Cited by 41 publications

References 15 publications

Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

A Survey of Mobility Management as a Service in Real-Time Inter/Intra Slice Control

Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes

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