Towards a virtual network function research agenda: A systematic literature review of VNF design considerations

Zhang, Chuanji; Joshi, Harshvardhan P.; Riley, George F.; Wright, Steven A.

doi:10.1016/j.jnca.2019.102417

Cited by 21 publications

(10 citation statements)

References 16 publications

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“…Currently, literature reviews exploring NFV applications, such as the ones in [3], [4], and [5], have been published. In particular, [3] studies integrated NFV/SDN architectures; it synthesizes the most relevant architectural projects in the literature, and identifies areas for further improvement.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, [3] studies integrated NFV/SDN architectures; it synthesizes the most relevant architectural projects in the literature, and identifies areas for further improvement. In [4], a general approach to the VNF design is provided, whereas in [5], VNF is studied in terms of flexibility. Despite the mentioned efforts, to the best of the authors' knowledge, none of them have specifically addressed the NFV applications in data centers.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, to achieve a higher quality of service (QoS) and adapt to the growing demand of users, further review of the computer network's mechanisms is required. Researchers have long discussed the resource allocation problem in computer networks and cloud computing [3], [4], less attention has been paid on resource allocation for virtualized functions in NFV data centers [6]. To bridge this research gap, we conducted a Systematic Literature Review (SLR) to synthesize the available knowledge concerning NFV applications in data centers.…”

Section: Introductionmentioning

confidence: 99%

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NFV Data Centers: A Systematic Review

2020

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This work was supported under Grants for Brazilian PhD students with Coordination for the Improvement of Higher Education Personnel (CAPES). ABSTRACT Recently, the widespread use of Network Function Virtualization (NFV) has meant a significant shift in communication and network cloud-based services. NFV increases network function, allocation, and flexibility, for the implementation of new network services, The benefits from optimizing resource allocation in NFV data center include automation of the operational processes, reducing the operational efforts, and lowering capital costs. Despite being a hot topic, NFV data centers are still in their early stages. In this sense, there are some aspects involved in the current NFV implementations that required further investigation, such as performance evaluation and managemen. This paper presents an in-depth analysis of NFV applications in data center. Therefore, a Systematic Literature Review (SLR) was conducted aimed at studying and synthesizing the available works in the NFV state-of-the-art. Consequently, about 1,408 papers have been analyzed and filtered, evidencing a total of 65 relevant studies. This paper documents the relevance of the 65 papers, as well as provides an indepth insight into the primary open research questions concerning NFV architecture. In particular, highlighting the need for consolidating the integration of NFV in data centers by solving problems related to performance, dependability, resource allocation, cost, management, and resource interoperability. Based on the conducted SLR, several research gaps and open challenges have been identified, allowing the authors to provide new propositions and suggested future research avenues. Finally, the obtained results have shown that there is a strong need for further collaboration between academia and industry to conduct quality research and advance NFV practical implementations. INDEX TERMS Network function virtualization (NFV), software defined networking (SDN), cloud computing, data center, service function chain (SFC), performance evaluation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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NFV Data Centers: A Systematic Review

2020

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“…Network Function Virtualization (NFV) [5,6] has become an important topic in recent years. NFV technology can decouple Network Functions (NFs) from proprietary and application-specific hardware to make them operate in software, known as Virtual Network Functions (VNFs) [7][8][9], on virtual instances, such as virtual machines and containers, running on Commercial-Off-The-Shelf (COTS) devices. Typically, multiple VNFs are chained in a particular order composing a Service Function Chain (SFC), which provides the services required by a user.…”

Section: Introductionmentioning

confidence: 99%

VNF Placement for Service Function Chains with Strong Low-Delay Restrictions in Edge Computing Networks

2020

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The edge computing paradigm, allowing the location of network services close to end users, defines new network scenarios. One of them considers the existence of micro data centers, with reduced resources but located closer to service requesters, to complement remote cloud data centers. This hierarchical and geo-distributed architecture allows the definition of different time constraints that can be taken into account when mapping services into data centers. This feature is especially useful in the Virtual Network Function (VNF) placement problem, where the network functions composing a Service Function Chain (SFC) may require more or less strong delay restrictions. We propose the ModPG (Modified Priority-based Greedy) heuristic, a VNF placement solution that weighs the latency, bandwidth, and resource restrictions, but also the instantiation cost of VNFs. ModPG is an improved solution of a previous proposal (called PG). Although both heuristics share the same optimization target, that is the reduction of the total substrate resource cost, the ModPG heuristic identifies and solves a limitation of the PG solution: the mapping of sets of SFCs that include a significant proportion of SFC requests with strong low-delay restrictions. Unlike PG heuristic performance evaluation, where the amount of SFC requests with strong low-delay restrictions is not considered as a factor to be analyzed, in this work, both solutions are compared considering the presence of 1%, 15%, and 25% of this type of SFC request. Results show that the ModPG heuristic optimizes the target cost similarly to the original proposal, and at the same time, it offers a better performance when a significant number of low-delay demanding SFC requests are present.

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“…There have been several kinds of research 60 considering VNF deployment in the context of heterogeneous 61 networks. A broad plethora of research studies related to VNF 62 deployment in the hybrid cloud has been emerging lately 63 [22] [23] [24]. To enhance the Quality-of-Experience (QoE) 64 of the users, massive computation time critical applications 65 require a large volume of communication resources.…”

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confidence: 99%

Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Emu

Choudhury

2021

IEEE Open J. Comput. Soc.

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Due to the massive Internet of Things (IoT) connectivity and substantial growth of communication traffic, Virtual Network Function (VNF) orchestration scheme is anticipated to function promptly, dynamically, and intelligently for next-generation networks. Hence, we urge the necessity to move beyond the traditional paradigm and employ VNFs on the network edge located cloudlet. Overall, multi-access edge computing can intensify the performance of delay-sensitive IoT applications compared to the core cloud based VNF deployments. In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions. We also address the challenges associated with state-of-the-art approaches to deal with dynamic network traffic and topology patterns. Our envisioned methods, based on Convolutional Neural Networks and Artificial Neural Networks named as E-ConvNets and E-ANN respectively, suggest two proactive VNF deployment strategies. These ensembled VNF deployment strategies demonstrate encouraging performance (optimality gap nearly 7%) in terms of minimizing relocation and communication costs, and high scalability intelligence factor (around 0.93) through simulation results compared to standalone deep learning models. Furthermore, the presented results indicate the potentialities of applying deep learning-based strategies into similar research enigmas for future telecommunication network researches.

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Towards a virtual network function research agenda: A systematic literature review of VNF design considerations

Cited by 21 publications

References 16 publications

NFV Data Centers: A Systematic Review

NFV Data Centers: A Systematic Review

VNF Placement for Service Function Chains with Strong Low-Delay Restrictions in Edge Computing Networks

Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

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