Design Challenges for High Performance, Scalable NFV Interconnects

Liu, Guyue; Ramakrishnan, K. K.; Schlansker, Michael; Tourrilhes, Jean; Wood, Timothy

doi:10.1145/3098583.3098592

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Scalability: Software-based IDSes incur high computational costs and often do not scale well (unlike specialized hardware appliances) to handle high traffic rates cost-effectively [165]. Ineffective design of software components can make this problem even worse [88]. Therefore, signature-based IDSes running software platforms are mostly used by relatively smaller enterprises with low traffic rates.…”

Section: B Community Signature-based Detectionmentioning

confidence: 99%

Classifying and tracking enterprise assets via dual-grained network behavioral analysis

Lyu

Gharakheili²,

Sivaraman³

2022

Computer Networks

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Section: B Community Signature-based Detectionmentioning

confidence: 99%

Classifying and tracking enterprise assets via dual-grained network behavioral analysis

Lyu

Gharakheili²,

Sivaraman³

2022

Computer Networks

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“…The experiment was performed by deploying Virtual Machines (VMs), which implements two different VNFs: Open virtual switch (OVS) [24,25] and Snort [25,26]. OVS is an open source implementation of SDN multilayer switch, while Snort is a network anomaly detection and prevention system, which can analyze the traffic.…”

Section: Virtual Network Functions Vnfsmentioning

confidence: 99%

Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks

et al. 2022

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Rapid development in the field of computer networking is now demanding the application of Machine Learning (ML) techniques in the traditional settings to improve the efficiency and bring automation to these networks. The application of ML to existing networks brings a lot of challenges and use-cases. In this context, we investigate different ML techniques to estimate resource requirements of complex network entities such as Virtual Network Functions (VNFs) deployed in Software Defined Networks (SDN) environment. In particular, we focus on the resource requirements of the VNFs in terms of Central Processing Unit (CPU) consumption, when input traffic represented by features is processed by them. We propose supervised ML models, Multiple Linear Regression (MLR) and Support Vector Regression (SVR), which are compared and analyzed against state of the art and use Fitting Neural Networks (FNN), to answer the resource requirement problem for VNF. Our experiments demonstrated that the behavior of different VNFs can be learned in order to model their resource requirements. Finally, these models are compared and analyzed, in terms of the regression accuracy and Cumulative Distribution Function (CDF) of the percentage prediction error. In all the investigated cases, the ML models achieved a good prediction accuracy with the total error less than 10% for FNN, while the total error was less than 9% and 4% for MLR and SVR, respectively, which shows the effectiveness of ML in solving such problems. Furthermore, the results shows that SVR outperform MLR and FNN in almost all the considered scenarios, while MLR is marginally more accurate than FNN.

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“…In this context, software-based network functions running on virtual machines and containers hosted in commodity servers seem to be the best option to provide high flexibility to create network functions whenever and wherever needed. However, the performance of such network functions in terms of instantiation time, processing capacity and latency as well isolation between their running environments (i.e., containers or virtual machines) still not high enough to be deployed in production environments [47], [48], [49], [50], [51]. This brings the need to further investigate server virtualization technologies, Linux kernel network, the I/O and CPU schedulers, and the inter-VM communication in order to maximize the packet processing capacity and the speed of the containers and virtual machines.…”

Section: F High-performance Virtual Network Functions and Servicesmentioning

confidence: 99%

FlexNGIA: A Flexible Internet Architecture for the Next-Generation Tactile Internet

Zhani

Elbakoury

2020

J Netw Syst Manage

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From virtual reality and telepresence, to augmented reality, holoportation, and remotely controlled robotics, these future network applications promise an unprecedented development for society, economics and culture by revolutionizing the way we live, learn, work and play. In order to deploy such futuristic applications and to cater to their performance requirements, recent trends stressed the need for the "Tactile Internet", an Internet that, according to the International Telecommunication Union (ITU), combines ultra low latency with extremely high availability, reliability and security [1]. Unfortunately, todays Internet falls short when it comes to providing such stringent requirements due to several fundamental limitations in the design of the current network architecture and communication protocols. This brings the need to rethink the network architecture and protocols, and efficiently harness recent technological advances in terms of virtualization and network softwarization to design the Tactile Internet of the future.In this paper, we start by analyzing the characteristics and requirements of future networking applications. We then highlight the limitations of the traditional network architecture and protocols and their inability to cater to these requirements. Afterward, we put forward a novel network architecture adapted to the Tactile Internet called FlexNGIA, a Flexible Next-Generation Internet Architecture 1 . We then describe some use-cases where we discuss the potential mechanisms and control loops that could be offered by FlexNGIA in order to ensure the required performance and reliability guarantees for future applications. Finally, we identify the key research challenges to further develop FlexNGIA towards a full-fledged architecture for the future Tactile Internet.

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Design Challenges for High Performance, Scalable NFV Interconnects

Cited by 9 publications

References 5 publications

Classifying and tracking enterprise assets via dual-grained network behavioral analysis

Classifying and tracking enterprise assets via dual-grained network behavioral analysis

Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks

FlexNGIA: A Flexible Internet Architecture for the Next-Generation Tactile Internet

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