R-Learning-Based Admission Control for Service Federation in Multi-domain 5G Networks

Bakhshi, Bahador; Mangues‐Bafalluy, Josep

doi:10.1109/globecom46510.2021.9685936

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…Bakhshi et al [41] propose a slice admission control in a federated environment formed by one consumer and provider domain. For a given slice request, the admission control decides whether to deploy the slice in the consumer or the provider domain or reject it.…”

Section: B Slice Admission Controlmentioning

confidence: 99%

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Donatti¹,

Corrêa

Martins

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.

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Section: B Slice Admission Controlmentioning

confidence: 99%

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Donatti¹,

Corrêa

Martins

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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“…Policy Iteration [38] is one of the dynamic algorithms in RL that is used to learn the optimal policy in an MDP environment when we are aware of transition probabilities. Similar to [39], we have applied policy iteration for our goal, which tries to maximize the long-term reward of the InP. It follows three steps to find the best policy as illustrated in Algorithm 1.…”

Section: B Dynamic Algorithm: Policy Iterationmentioning

confidence: 99%

DRL-Based Slice Admission Using Overbooking in 5G Networks

Saxena

Sivalingam

2023

IEEE Open J. Commun. Soc.

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In 5G-and-beyond networks, the concept of Network Slicing is used to support multiple independent and co-existing logical networks on a physical network infrastructure. The infrastructure provider (InP) owns the set of virtual and physical resources that are used to support the tenant slice requests. Each slice request specifies a service level agreement (SLA) that contains the required slice-level resources (computation and communication) and the revenue provided by the tenant. Due to limited resources, the InP cannot accommodate all requests made by the tenants. In general, it has been found that tenants tend to overestimate their resource demands (e.g. for 5G Core computation) to reduce possible SLA violations. In this paper, we consider two major slice types: Elastic (low priority and low revenue) and Inelastic (high priority and high revenue). We apply the concept of overbooking, where the InP accepts more slices while considering slice priorities in order to maximize the overall revenue and utilization. We consider a multi-tenant environment and propose a slice admission system named PRLOV, which is based on Reinforcement Learning (RL) and prediction methods. The system predicts the future resource demands of Elastic slices and applies an opportunistic overbooking technique to overbook InP for accepting more slices. In addition, the admission decision is formulated as an Markov Decision Process (MDP) problem and solved using standard RL techniques (Policy Iteration, Q-Learning, DQN). The performance of our proposed work is compared against three other heuristics (Basic, Prediction, PRL) that do not use overbooking. Data traces from the Materna data center networks were used for prediction purposes. The important performance metrics measures include InP total revenue, the acceptance rate of respective slices and overall resource utilization for different slices. The results show that the proposed work significantly outperforms the other mechanisms in terms of revenue gain and resource utilization. Simulation results show that PRLOV provides a revenue gain of 6%, 26%, and 102% compared to the PRL, Prediction and Basic scheme.

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“…In [7], a reinforcement-learning (RL) based admission control is proposed to maximize profit. The RL algorithm improves revenue but will not perform optimally when the state space grows.…”

Section: Related Workmentioning

confidence: 99%

Novel Slice Admission Control Scheme with Overbooking and Dynamic Buyback Process

Yese,

Berri,

Chorti

2023

2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)

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The emergence of 5G systems brought to fore the importance of network slicing (NS) as it allows infrastructure providers (InPs) to create logical networks (slices) and virtually share network resources to their tenants. However, due to the limited resources of the InP, the resource management algorithms like resource allocation and admission control are required to ensure efficient management of these scarce resources. Indeed, admission control algorithms play a critical role of regulating access to the network, by determining whether a slice request should be accepted or not with respect to some standards such as maximizing the InP's revenue and maintaining service level agreements (SLAs). In this paper, we propose an admission control algorithm that employs the concept of overbooking to admit slice requests beyond the InP's nominal available resources. Moreover, we employ a dynamic queue adaption priority, stepwise pooling and dynamic buyback price mechanism to ensure efficient and profitable admission decision for the InP. We assess the performance of the proposed algorithm against state of the art (SOTA) solution considering different priority schemes. The results show that the proposed solution outperforms the SOTA solution as it yields i) higher revenue, ii) lower buyback cost and iii) higher net revenue for the InP while still maintaining a marginally higher slice acceptance rate.

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R-Learning-Based Admission Control for Service Federation in Multi-domain 5G Networks

Cited by 11 publications

References 20 publications

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

DRL-Based Slice Admission Using Overbooking in 5G Networks

Novel Slice Admission Control Scheme with Overbooking and Dynamic Buyback Process

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