A effective two-step strategy of multi-domain virtual network embedding in 5G network slicing

Wang, Xiaolei; Chen, Qiang; Qiu, Hang

doi:10.1109/compcomm.2017.8322728

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…Numerous approaches (e.g., [18]- [23], among others) based on network embedding have also been published in recent years. In particular, the authors in [18] proposed a virtual network embedding problem based on 3-D resources that includes computing, network, and storage; [19] looked into a network slice embedding problem that considers the deployment costs due to the slice's minimum resource requirements (both at the nodes and links), as well as a cost related to the end-to-end delay (i.e., propagation and processing delays plus the virtualization overhead); [20] proposed an efficient heuristic for network slice embedding that allocates the network slice resources based on node rankings (four possible ranking algorithms are evaluated); and [21] proposed an algorithm for automatic virtual network embedding based on deep reinforcement learning with a novel multi-objective reward function.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, the authors in [18] proposed a virtual network embedding problem based on 3-D resources that includes computing, network, and storage; [19] looked into a network slice embedding problem that considers the deployment costs due to the slice's minimum resource requirements (both at the nodes and links), as well as a cost related to the end-to-end delay (i.e., propagation and processing delays plus the virtualization overhead); [20] proposed an efficient heuristic for network slice embedding that allocates the network slice resources based on node rankings (four possible ranking algorithms are evaluated); and [21] proposed an algorithm for automatic virtual network embedding based on deep reinforcement learning with a novel multi-objective reward function. Meanwhile, [22] and [23] investigated the partitioning of multi-domain virtual networks and proposed heuristics based on particle swarm optimization; the latter also proposed a two-step embedding strategy for the inter-and intradomain embedding sub-problems.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Site Resource Allocation in a QoS-Aware 5G Infrastructure

Bolla

Bruschi

Davoli

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Network softwarization has paved the way for 5G technologies, and a wide-range of (radically new) verticals. As the telecommunications infrastructure evolves into a sort of distributed datacenter, multiple tenants such as vertical industries and network service providers share its aggregate pool of resources (e.g., networking, computing, etc.) in a layered "as-a-Service" approach exposed as slice abstractions. The challenge remains in the coordination of various stakeholders' assets in realizing end-to-end network slices and supporting the multi-site deployment and chaining of the micro-service components needed to implement cloud-native vertical applications (vApps). In this context, particular care must be taken to ensure that the required resources are identified, made available and managed in a way that satisfies the vApp requirements, allows for a fair share of resources and has a reasonable impact on the overall vApp deployment time. With these challenges in mind, this paper presents the Resource Selection Optimizer (RSO) -a softwareservice in the MATILDA Operations Support System (OSS), whose main goal is to select the most appropriate network and computing resources (according to some criterion) among a list of options provided by the Wide-area Infrastructure Manager (WIM). It consists of three submodules that respectively handle: (i) the aggregation of vApp components based on affinities, (ii) the forecasting of (micro-) datacenter resources utilization, (iii) and the multi-site placement of the (aggregated) vApp micro-service components. The RSO's performance is mainly evaluated in terms of the execution times of its submodules while varying their respective input parameters, and additionally, three selection policies are also compared. Experimental results aim to highlight the RSO behavior in both execution times and deployment costs, as well as the RSO interactions with other OSS submodules and network platform components, not only for multi-site vApp deployment but also for other network/services management operations.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi-Site Resource Allocation in a QoS-Aware 5G Infrastructure

Bolla

Bruschi

Davoli

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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show abstract

“…Due to the selfishness of different InPs, they are reluctant to disclose the specific topology information of networks. Therefore, the authors of literature [10] proposed a effective two-step strategy of multi-domain virtual network embedding in 5G network slicing, which can dynamically manage the resource information of different domains. In literature [11], the authors proposed a new information-centric heterogeneous network framework, which gives nodes the ability to store and compute, thus improving data throughput.…”

Section: The Distributed Vne Algorithmsmentioning

confidence: 99%

A multi-domain virtual network embedding algorithm with delay prediction

Zhang¹,

Pang²,

Ni³

et al. 2022

Preprint

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Virtual network embedding (VNE) is an crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN). With the emergence of various delay-sensitive applications, how to improve the delay performance of the system has become a hot topic in academic circles. Based on extensive research, we proposed a multi-domain virtual network embedding algorithm based on delay prediction (DP-VNE). Firstly, the candidate physical nodes are selected by estimating the delay of virtual requests, then particle swarm optimization (PSO) algorithm is used to optimize the mapping process, so as to reduce the delay of the system. The simulation results show that compared with the other three advanced algorithms, the proposed algorithm can significantly reduce the system delay while keeping other indicators unaffected.

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“…Meanwhile, two time‐efficient heuristic algorithms were designed to solve the model by leveraging the minimum k ‐cut problem. In their study, Wang et al 36 presented a two‐step cross‐domain SFC embedding strategy to dynamically manage network resources in each autonomous domain. The optimized discrete particle swarm algorithm‐based cross‐domain SFC embedding method was proposed to reduce resource consumption cost, and then, a fast algorithm based on Kruskal minimum spanning tree was proposed for intra‐domain mapping.…”

Section: The State Of the Art Of Multi‐domain Sfc Deploymentmentioning

confidence: 99%

The intelligent multi‐domain service function chain deployment: Architecture, challenges and solutions

Zhang

Wang

Dong

et al. 2020

Int J Communication

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Network function virtualization (NFV) technology achieves flexible service deployment by replacing the middleboxes with virtual network functions (VNFs). In NFV, a set of VNFs are chained in a given order, called service function chain (SFC), and accordingly, data flow is steered to traverse all the VNFs in order to offer a service. With a large number of network devices and end users being connected into Internet, there is a growing demand for largescale multi-domain networks to dynamically deploy the SFC across multiple network domains, in order to support efficient service provisioning. To this end, in this paper, we first investigate the state of the art of multi-domain SFC deployment, and then propose an intelligent multi-domain SFC deployment (IMSD) architecture by leveraging software-defined networking (SDN), NFV, and deep learning technologies. Furthermore, we discuss the potential challenges to realize the IMSD and provide some promising solutions. K E Y W O R D S deep learning, multi-domain networks, SDN, SFC deployment 1 | INTRODUCTION Recently, a great variety of middleboxes have been designed to efficiently provide various network services. 1 However, the service provisioning method is not flexible and leads to large capital expenditure (CAPEX) and operational expenditure (OPEX) since the middleboxes are heavily dependent on specific hardware devices. Fortunately, the emergence of network function virtualization (NFV) technology provides a promising solution to address the issues. NFV decouples network functions from hardware devices and implements them in software, called virtual network functions (VNFs). 2,3 Unlike the middleboxes, the VNFs can be flexibly deployed in the commercial-off-the-shelf devices. NFV

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A effective two-step strategy of multi-domain virtual network embedding in 5G network slicing

Cited by 10 publications

References 8 publications

Multi-Site Resource Allocation in a QoS-Aware 5G Infrastructure

Multi-Site Resource Allocation in a QoS-Aware 5G Infrastructure

A multi-domain virtual network embedding algorithm with delay prediction

The intelligent multi‐domain service function chain deployment: Architecture, challenges and solutions

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