Latency Aware VNF Deployment at Edge Devices for IoT Services: An Artificial Neural Network Based Approach

Emu, Mahzabeen; Yan, Peizhi; Choudhury, Salimur

doi:10.1109/iccworkshops49005.2020.9145242

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…As follows, we discuss such proposals. In Table Mono-objective approaches [14][15][16][17][18]30] solve the VNF placement problem by using only one characteristic of the environment, such as the resource consumption in edge nodes and, in this case, they try to minimize it. For instance, authors in [30] studied the VNF sharing problem aiming to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, authors in [30] studied the VNF sharing problem aiming to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay. Emu et al [16] proposed a solution to the VNF placement problem using an ILP model that guarantees the minimum end-to-end latency while ensuring QoS by not overstepping beyond an acceptable limit of latency violation. The authors used the ILP solutions to train an artificial neural network (ANN) to cope with the NP-hardness of such a problem.…”

Section: Related Workmentioning

confidence: 99%

“…Such approaches quickly converge to a solution but tend to fall into local minimum or maximum. Other approaches consider machine learning-based strategies [16,19,23,24], which typically require datasets in training and testing phases, sometimes difficult to obtain, hindering their practical deployment in dynamic environments.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Strategy for VNF Placement in Edge Computing Environments

et al. 2022

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Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Novel Strategy for VNF Placement in Edge Computing Environments

et al. 2022

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“…In [30], joint user association and VNF placement problem is considered for providing latency sensitive applications using MEC in 5G networks with the goal of minimizing the service provisioning cost. In [31], latency-aware VNF deployment problem is considered at edge for IoT services with the goal of minimizing the e2e latency. In [32], latency-aware VNF placement and assignment problem is considered in MEC with the goal of maximizing the number of admitted service requests.…”

Section: Background and Related Workmentioning

confidence: 99%

Resilient and Latency-aware Orchestration of Network Slices Using Multi-connectivity in MEC-enabled 5G Networks

Thiruvasagam,

Chakraborty,

Murthy

2021

Preprint

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Network slicing and multi-access edge computing (MEC) are new paradigms which play key roles in 5G and beyond networks. In particular, network slicing allows network operators (NOs) to divide the available network resources into multiple logical network slices (NSs) for providing dedicated virtual networks tailored to the specific service/business requirements. MEC enables NOs to provide diverse ultra-low latency services for supporting the needs of different industry verticals by moving computing facilities to the network edge. An NS can be constructed/deployed by instantiating a set of virtual network functions (VNFs) on top of MEC cloud servers for provisioning diverse latency-sensitive/time-critical communication services (e.g., autonomous driving and augmented reality) on demand at a lesser cost and time. However, VNFs, MEC cloud servers, and communication links are subject to failures due to software bugs, misconfiguration, overloading, hardware faults, cyber attacks, power outage, and natural/man-made disaster. Failure of a critical network component disrupts services abruptly and leads to users' dissatisfaction, which may result in revenue loss for the NOs. In this paper, we present a novel approach based on multi-connectivity in 5G networks to tackle this problem and our proposed approach is resilient against i) failure of VNFs, ii) failure of local servers within MEC, iii) failure of communication links, and iv) failure of an entire MEC cloud facility in regional level. To this end, we formulate the problem as a binary integer programming (BIP) model in order to optimally deploy NSs with the minimum cost, and prove it is NP-hard. Since the exact optimal solution for the NP-hard problem cannot be efficiently computed in polynomial time, we propose an efficient genetic algorithm based heuristic to obtain near-optimal solution in polynomial time. By extensive simulations, we show that our proposed approach not only reduces resource wastage, but also improves throughput while providing high resiliency against failures.

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“…The joint problem of resource allocation in cloud-network integrated infrastructures has been extensively studied in the literature. In [5], the authors examined the VNF placement problem as an Integer Linear Programming (ILP) model that assures the minimal End-to-End (E2E) latency while maintaining QoS requirements by not exceeding an acceptable latency violation limit. They suggested an approach based on neural networks and demonstrated that it can produce near-optimal solutions in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

Near-optimal Cloud-Network Integrated Resource Allocation for Latency-Sensitive B5G

Shokrnezhad

Taleb

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Nowadays, while the demand for capacity continues to expand, the blossoming of Internet of Everything is bringing in a paradigm shift to new perceptions of communication networks, ushering in a plethora of totally unique services. To provide these services, Virtual Network Functions (VNFs) must be established and reachable by end-users, which will generate and consume massive volumes of data that must be processed locally for service responsiveness and scalability. For this to be realized, a solid cloud-network Integrated infrastructure is a necessity, and since cloud and network domains would be diverse in terms of characteristics but limited in terms of capability, communication and computing resources should be jointly controlled to unleash its full potential. Although several innovative methods have been proposed to allocate the resources, most of them either ignored network resources or relaxed the network as a simple graph, which are not applicable to Beyond 5G because of its dynamism and stringent QoS requirements. This paper fills in the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including VNF placement and assignment, traffic prioritization, and path selection considering capacity constraints as well as link and queuing delays, with the goal of minimizing overall cost. We formulate the problem as a non-linear programming model, and propose two approaches, dubbed B&B-CCRA and WF-CCRA respectively, based on the Branch & Bound and Water-Filling algorithms. Numerical simulations show that B&B-CCRA can solve the problem optimally, whereas WF-CCRA can provide near-optimal solutions in significantly less time.

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Latency Aware VNF Deployment at Edge Devices for IoT Services: An Artificial Neural Network Based Approach

Cited by 15 publications

References 20 publications

A Novel Strategy for VNF Placement in Edge Computing Environments

A Novel Strategy for VNF Placement in Edge Computing Environments

Resilient and Latency-aware Orchestration of Network Slices Using Multi-connectivity in MEC-enabled 5G Networks

Near-optimal Cloud-Network Integrated Resource Allocation for Latency-Sensitive B5G

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