Provisioning Optimization for Determining and Embedding 5G End-to-End Information Centric Network Slice

Liu, Jiayi; Zhao, Bowen; Shao, Menghan; Yang, Qinghai; Simon, Gwendal

doi:10.1109/tnsm.2020.3045051

Cited by 24 publications

(12 citation statements)

References 35 publications

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“…On the basis of the limitations of computational, network, and storage resources, [26] proposed two fundamental VNE techniques, namely node ranking metric for virtual network embedding (NRM-VNE) and resource consumption ratio for virtual network embedding (RCR-VNE). Authors of [27] suggested a heuristic technique known as information-centric networking-network slice determination and embedding to address challenges associated with network embedding in large-scale networks. The primary value of the proposed algorithm is that it solves the problem without requiring any background information about the topology of the network or the availability of its resources.…”

Section: Slice Isolation and Management Strategiesmentioning

confidence: 99%

Slice-aware 5G Network Orchestration Framework Based on Dual-Slice Isolation and Management Strategy (D-SIMS)

Venkatapathy,

Sriniva,

Lee

et al. 2023

Preprint

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Network slicing is crucial to the 5G architecture because it enables the virtual-ization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two 1 phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijk-stra’s algorithm for link mapping. During the slice creation phase, the proposed DNN training method’s classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation , metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.

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Section: Slice Isolation and Management Strategiesmentioning

confidence: 99%

Slice-aware 5G Network Orchestration Framework Based on Dual-Slice Isolation and Management Strategy (D-SIMS)

Venkatapathy,

Sriniva,

Lee

et al. 2023

Preprint

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show abstract

“…In the first phase (step [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] where we try to solve computing resource deficit for physical nodes in the set (CN − BN), we do not treat link congestion, because virtual node migration leads to virtual link migration which could change the bandwidth state of physical links. After a physical node's computing resource deficit is solved, we check whether the bandwidth resource deficit still exists, if so the physical node's congestion level is recomputed and insert in to the BN set, and it will be treat in the second phase of the algorithm (step 21-27).…”

Section: Reconfiguration Algorithmmentioning

confidence: 99%

“…The concept of NS spans across the 5G network: from the Radio Access Network (RAN), go through the Transport Network (TN) and the Core Network (CN). In the scientific literature, studies related to 5G NS are classified into two categories [3]: those that focus on virtualizing and softwarizing the radio resources for RAN slicing [4]- [6] and those, referred to as Core slicing, that investigate the placement of Virtual Network Function (VNF) instances on the underlying physical infrastructure [7], [8]. The current paper belongs to the second category.…”

Section: Introductionmentioning

confidence: 99%

Deployment and Reconfiguration for Balanced 5G Core Network Slices

Lü

Wang

Pang

et al. 2021

IEICE Trans. Fundamentals

Self Cite

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Network Slicing (NS) is recognized as a key technology for the 5G network in providing tailored network services towards various types of verticals over a shared physical infrastructure. It offers the flexibility of on-demand provisioning of diverse services based on tenants' requirements in a dynamic environment. In this work, we focus on two important issues related to 5G Core slices: the deployment and the reconfiguration of 5G Core NSs. Firstly, for slice deployment, balancing the workloads of the underlying network is beneficial in mitigating resource fragmentation for accommodating the future unknown network slice requests. In this vein, we formulate a load-balancing oriented 5G Core NS deployment problem through an Integer Linear Program (ILP) formulation. Further, for slice reconfiguration, we propose a reactive strategy to accommodate a rejected NS request by reorganizing the already-deployed NSs. Typically, the NS deployment algorithm is reutilized with slacked physical resources to find out the congested part of the network, due to which the NS is rejected. Then, these congested physical nodes and links are reconfigured by migrating virtual network functions and virtual links, to re-balance the utilization of the whole physical network. To evaluate the performance of deployment and reconfiguration algorithms we proposed, extensive simulations have been conducted. The results show that our deployment algorithm performs better in resource balancing, hence achieves higher acceptance ratio by comparing to existing works. Moreover, our reconfiguration algorithm improves resource utilization by accommodating more NSs in a dynamic environment.

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“…Therefore, the combined structure, i.e. Software defined‐information centric network (SD‐ICN), has been paid close attention by a multitude of school groups and enterprises [10–30]. After both architectures interact with each other, SD‐ICN can complement respective advantages and sufficiently make up for a series of drawbacks [31].…”

Section: Introductionmentioning

confidence: 99%

Survey on the scheme evaluation, opportunities and challenges of software defined‐information centric network

Ai,

Zhang,

Zhang

et al. 2023

IET Communications

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As a promising architecture of next‐generation network, software defined‐information centric network (SD‐ICN) inherits the advantages of software defined network (SDN) and information‐centric network (ICN) to enable flexible and fast content retrieval, especially in the current era of artificial intelligence. However, the existing researches mainly focus on a single respective in this field, which motivates in comprehensively providing a forward‐looking guidance and development direction for scholars and engineers. To this end, the latest developments of SD‐ICN is presented. First, the widely‐accepted concepts and impacts on traditional networks are introduced. Second, the shortcomings of SDN and ICN over conventional networks are respectively analyzed to illustrate the necessity of SD‐ICN. Third, based on extensive analysis and deep deliberation, a methodical taxonomy for existing combination studies is proposed. They are divided into SDN over ICN, ICN over SDN, and mutual immersive pattern. Fourth, the performances of three integration categories are compared and the limitations of related works are highlighted. Fifth, the maturity index from six development indicators are evaluated. Further, the maturity and practicality of these schemes are generalized. Based on the above studies and comparisons, the lessons learned by SDN and ICN developments are concluded. Finally, future research directions and opportunities are discussed for the readers.

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Provisioning Optimization for Determining and Embedding 5G End-to-End Information Centric Network Slice

Cited by 24 publications

References 35 publications

Slice-aware 5G Network Orchestration Framework Based on Dual-Slice Isolation and Management Strategy (D-SIMS)

Slice-aware 5G Network Orchestration Framework Based on Dual-Slice Isolation and Management Strategy (D-SIMS)

Deployment and Reconfiguration for Balanced 5G Core Network Slices

Survey on the scheme evaluation, opportunities and challenges of software defined‐information centric network

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