On service-chaining strategies using Virtual Network Functions in operator networks

Gupta, Abhishek; Habib, M. Farhan; Mandal, Uttam Kumar; Chowdhury, Pulak; Tornatore, Massimo; Mukherjee, Biswanath

doi:10.1016/j.comnet.2018.01.028

Cited by 50 publications

(49 citation statements)

References 23 publications

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“…However, the solutions proposed in [13,14] make use of Virtual Network Functions combined with SDN with objective to provide more flexible live video streaming platform which would make the management operations for Telco operators. The VNFs are deployed according to pre-defined QoS parameters by each client and traffic control rules applied to shape the service chain.…”

Section: Related Workmentioning

confidence: 99%

Model-based approach for semantic-driven deployment of containerized applications to support future internet services and architectures

Petrović

2019

Serb J Electr Eng

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Due to increasing number of connected Internet of Things (IoT) devices, enormous amount of data needs to be transmitted to the Cloud for processing, while the network is becoming Cloud computing's bottleneck. On the other side, the privacy and security issues in more sensitive application domains could dramatically restrict the freedom of data movement, so it is not possible to offload all the data to the Cloud for processing. Furthermore, the manual operations related to tuning and deployment of these applications are time-consuming and require additional effort. In this paper, a model-based framework for automated, semantic-driven (re-)deployment of containerized applications is presented, leveraging the synergy of Virtual Network Functions (VNFs) and SDN, tackling the mentioned issues.

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Section: Related Workmentioning

confidence: 99%

Model-based approach for semantic-driven deployment of containerized applications to support future internet services and architectures

Petrović

2019

Serb J Electr Eng

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“…For each of the four offices, VNFs are deployed on servers. For our study, we assume that a service chain [26] with three VNF services (namely, firewalls, routers, and private branch exchanges (PBXs)) are deployed in all sites. We also assume that each office experiences traffic load same as the two-day test dataset.…”

Section: H Impact Of Auto-scaling On Leasing Costmentioning

confidence: 99%

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

Rahman

Ahmed

Huynh

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on autoscaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., startup time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report indepth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers. 1

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“…Various models have been built using MIQCP [21], MILP [22][23][24], and ILP [12,[25][26][27], which optimize different parameters such as end-toend latency and resource utilization. We analyze them from the aspect of their solving methods.…”

Section: Related Workmentioning

confidence: 99%

“…But the graph needs to be updated frequently. Reference [26] compares the effects of different deployment strategies of VNFs on network resource consumption. But it does not illustrate how to solve the developed model.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Delivering Customized Security Everywhere: Security Service Chain

Liu

Zhang

Liu

et al. 2017

Security and Communication Networks

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Security functions are usually deployed on proprietary hardware, which makes the delivery of security service inflexible and of high cost. Emerging technologies such as software-defined networking and network function virtualization go in the direction of executing functions as software components in virtual machines or containers provisioned in standard hardware resources. They enable network to provide customized security service by deploying Security Service Chain (SSC), which refers to steering flow through multiple security functions in a particular order specified by individual user or application. However, SSC Deployment Problem (SSC-DP) needs to be solved. It is a challenging problem for various reasons, such as the heterogeneity of instances in terms of service capacity and resource demand. In this paper, we propose an SSC-based approach to deliver security service to users without worrying about physical locations of security functions. For SSC-DP, we present a three-phase method to solve it while optimizing network and security resource allocation. The presented method allows network to serve a large number of flows and minimizes the latency seen by flows. Comparative experiments on the fat-tree and Waxman topologies show that our method performs better than other heuristics under a wide range of network conditions.

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On service-chaining strategies using Virtual Network Functions in operator networks

Cited by 50 publications

References 23 publications

Model-based approach for semantic-driven deployment of containerized applications to support future internet services and architectures

Model-based approach for semantic-driven deployment of containerized applications to support future internet services and architectures

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

A New Approach for Delivering Customized Security Everywhere: Security Service Chain

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