A Survey on Slice Admission Control Strategies and Optimization Schemes in 5G Network

Ojijo, Mourice Otieno; Falowo, Olabisi E.

doi:10.1109/access.2020.2967626

Cited by 54 publications

(36 citation statements)

References 58 publications

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“…Under the reservation-based approach, the network infrastructure needs to determine whether a certain reservation of a network slice can be admitted while meeting the Service Level Agreement of the new slice as well as of the other slices being served. A number of algorithms have been proposed in the literature to this end (see, e.g., [17], [27]). Since slices may not always use their allocated resources, it may be possible to exploit multiplexing gains in order to improve the overall efficiency.…”

Section: Realizing Resource Allocations With Reservation and Shamentioning

confidence: 99%

Resource Allocation for Network Slicing in Mobile Networks

et al. 2020

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This paper provides a survey of resource allocation for network slicing. We focus on two classes of existing solutions: (i) reservation-based approaches, which allocate resources on a reservation basis, and (ii) share-based approaches, which allocate resources based on static overall shares associated to individual slices. We identify the requirements that a slice-based resource allocation mechanism should satisfy, and evaluate the performance of both approaches against these requirements. Our analysis reveals that reservation-based approaches provide a better level of isolation as well as stricter guarantees, by enabling tenants to explicitly reserve resources, but one must pay a price in terms of efficiency unless reservations can be updated very dynamically; in particular, efficiency falls below 50% when reservations are performed over long timescales. We provide further comparisons in terms of customizability, complexity, privacy and cost predictability, and discuss which approach might be more suitable depending on the network slices' characteristics. We also describe the additional mechanisms required to implement the desired resource allocations while meeting the latency and reliability requirements of the different slice types, and outline some issues for future work.

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Section: Realizing Resource Allocations With Reservation and Shamentioning

confidence: 99%

Resource Allocation for Network Slicing in Mobile Networks

et al. 2020

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“…In 5G/B5G networks, many factors should or can be considered as objectives of SAC algorithms [6], mainly are resource constraint, priority of services, fairness of resource sharing and profits earned [7]. Resource constraint is the most basic target and must be met, because each slice is attached a Service Level Agreement (SLA) which can only be guaranteed when obtained the necessary amount of resources [8], [9], the risk of destruction in isolation between slices will rise if resource squeeze happens.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks

Dai¹,

Lu²,

Ren³

et al. 2022

Preprint

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5G/B5G is envisioned to support various services with the assistance of network slices, each slice instance asks for adequate resources to provide the pre-negotiated service quality to its subscribers. Slice Admission Control (SAC) algorithm is a necessity for Slice Providers (SPs) to guarantee the QoS of each admitted request. Except for resource restrictions, more factors need to be considered in the sliced environment during the admission process, such as priority and fairness. The former relates to the innate characteristics of different services, while the latter matters because slices are instantiated on the shared physical equipments. In this paper, we investigate the SAC problem in 5G/B5G networks, taking priority and fairness into account simultaneously. Reinterpretation of the two intuitively conflicting concepts are proposed in advance to cope with this special scenario, then an online SAC algorithm called Prioritized Slice Admission Control Considering Fairness, PSACCF in short, is designed to help maintaining priority and achieving an adjustable degree of fairness under the assumption of rational service subscribers. Numerous simulations are carried out and the results show that PSACCF can effectively enhance the fairness indicator without compromising the priority requirement, and exhibits stronger stability and adaptability over existing policies.

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“…The AC should manage the local resources of the consumer domain and the federation quota of the provider domain to maximize the profit. While several AC algorithms in 5G networks have already been proposed [5], they consider single domain networks; and consequently are not directly applicable to the ACSF problem. This is the research gap that we aim to address.…”

Section: Introductionmentioning

confidence: 99%

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

Bakhshi¹,

Mangues‐Bafalluy²

2021

2021 IEEE Global Communications Conference (GLOBECOM)

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Network service federation in 5G/B5G networks enables service providers to extend service offering by collaborating with peering providers. Realizing this vision requires interoperability among providers towards end-to-end service orchestration across multiple administrative domains. Smart admission control is fundamental to make such extended offering profitable. Without prior knowledge of service requests, the admission controller (AC) either determines the domain to deploy the demand or rejects it to maximize the long-term average profit. In this paper, we first obtain the optimal AC policy by formulating the problem as a Markov decision process, which is solved through the policy iteration method. This provides the theoretical performance bound under the assumption of known arrival and departure rates of demands. Then, for practical solutions to be deployed in real systems, where the rates are not known, we apply the Q-Learning and R-Learning algorithms to approximate the optimal policy. The extensive simulation results show that learning approaches outperform the greedy policy and are capable of getting close to optimal performance. More specifically, R-learning always outperformed the rest of practical solutions and achieved an optimality gap of 3-5% independent of the system configuration, while Q-Learning showed lower performance and depended on discount factor tuning.

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A Survey on Slice Admission Control Strategies and Optimization Schemes in 5G Network

Cited by 54 publications

References 58 publications

Resource Allocation for Network Slicing in Mobile Networks

Resource Allocation for Network Slicing in Mobile Networks

PSACCF: Prioritized Online Slice Admission Control Considering Fairness in 5G/B5G Networks

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

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