Network Function Virtualization Networks Research Articles

Stochastic Performance Analysis of Network Function Virtualization in Future Internet

Network function virtualization (NFV) has been considered as a promising technology for future Internet to increase the network flexibility, accelerate the service innovation, and reduce the Capital Expenditures and Operational Expenditures costs through migrating network functions from dedicated network devices to commodity hardware. Recent studies reveal that although this migration of network function brings the network operation unprecedented flexibility and controllability, NFV-based architecture suffers from serious performance degradation compared with traditional service provisioning on dedicated devices. In order to achieve a comprehensive understanding of the service provisioning capability of NFV, this paper proposes a novel analytical model based on Stochastic Network Calculus (SNC) to quantitatively investigate the end-to-end performance bound of the NFV networks. To capture the dynamic and on-demand NFV features, both the non-bursty traffic, e.g., the Poisson process, and the bursty traffic, e.g., the Markov Modulated Poisson Process, are jointly considered in the developed model to characterize the arriving traffic. To address the challenges of resource competition and end-to-end NFV chaining, the property of convolution associativity and leftover service technologies of SNC are exploited to calculate the available resources of the Virtual Network Function nodes in the presence of multiple competing traffic and transfer the complex NFV chain into an equivalent system for performance derivation and analysis. Both the numerical analysis and extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model. Results demonstrate that the analytical performance metrics match well with those obtained from the simulation experiments and numerical analysis. In addition, the developed model is used as a practical and cost-effective tool to investigate the strategies of the service chain design and resource allocations in the NFV networks.

Matrix Differential Decomposition-Based Anomaly Detection and Localization in NFV Networks

Network function virtualization (NFV) is a promising network paradigm that enables the design and implementation of novel network services with lower cost, increased agility, and faster time-to-value. However, network anomalies caused by software malfunction, hardware failure, mis-configuration, or cyber attacks can greatly degrade the performance of NFV networks. A few matrix decomposition-based methods have shown their effectiveness in finding the existence of network-wide anomalies. However, a little attention has been paid to multiple anomalies detection and anomaly devices localization. To bridge this gap, in this paper, we propose a matrix differential decomposition (MDD)-based anomaly detection and localization algorithm for NFV networks. First, an NFV network prototype is built to investigate the property of NFV networks, and the effectiveness of traditional anomaly detection methods is evaluated. Second, we detail the MDD-based Anomaly DEtection and Localization (MADEL) algorithm. Finally, a series of experiments are conducted on three different NFV networks to evaluate the performance of the proposed algorithm. Experimental results show that the MADEL algorithm could effectively detect and localize different types of network anomalies.

AI-based software-defined virtual network function scheduling with delay optimization

AI-based network function virtualization (NFV) is an emerging technique that separates network control functionality from dedicated hardware middleboxes and is virtualized to reduce capital and operational costs. With the advances of NFV and AI-based software-defined networks, dynamic network service demands can be flexibly and effectively accomplished by connecting multiple virtual network functions (VNFs) running on virtual machines. However, such promising technology also introduces several new research challenges. Due to resource constraints, service providers may have to deploy different service function chains (SFCs) to share the same physical resources. Such sharing inevitably forces the scheduling of the SFCs and resources, which consumes computational time and introduces problems associated with reducing the response delay. In this paper, we address this challenge by developing two dynamic priority methods for queuing AI-based VNFs/services to improve the user experience. We account for both transmission and processing delays in our proposed algorithms and achieve a new processing order (scheduler) for VNFs to minimize the overall scheduling delay. The simulation results indicate that the proposed scheme can promote the performance of AI-based VNFs/services to meet strict latency requirements.

Exploiting Congestion Games to Achieve Distributed Service Chaining in NFV Networks

The network function virtualization (NFV) paradigm has gained increasing interest in both academia and industry as it promises scalable and flexible network management and orchestration. In NFV networks, network services are provided as chains of different virtual network functions (VNFs), which are instantiated and executed on dedicated VNF-compliant servers. The problem of composing those chains is referred to as the service chain composition problem. In contrast to centralized solutions that suffer from scalability and privacy issues, in this paper, we leverage non-cooperative game theory to achieve a low-complexity distributed solution to the above-mentioned problem. Specifically, to account for selfish and competitive behavior of users, we formulate the service chain composition problem as an atomic weighted congestion game with unsplittable flows and player-specific cost functions. We show that the game possesses a weighted potential function and admits a Nash equilibrium (NE). We prove that the price of anarchy is upper-bounded, and also propose a distributed and privacy-preserving algorithm which provably converges toward an NE of the game in polynomial time. Finally, through extensive numerical results, we assess the performance of the proposed distributed solution to the service chain composition problem.

Using the cooperative game for service placement of virtual network functions

To address the issues that middleboxes as a fundamental part of today's networks are facing, Network Function Virtualization (NFV) has been recently proposed, which in essence asserts to migrate hardware-based middleboxes into software-based virtualized function entities. Due to the demands of virtual services placement in NFV network environment, this paper models the service amount placement problem involving with the resources allocation as a cooperative game and proposes the placement policy by Nash Bargaining Solution (NBS). Specifically, we first introduce the system overview and apply the rigorous cooperative game-theoretic guide to build the mathematical model, which can give consideration to both the responding efficiency of service requirements and the allocation fairness. Then a distributed algorithm corresponding to NBS is designed to achieve predictable network performance for virtual instances placement. Finally, with simulations under various scenarios, the results show that our placement approach can achieve high utilization of network through the analysis of evaluation metrics namely the satisfaction degree and fairness index. With the suitable demand amount of services, the average values of two metrics can reach above 90%. And by tuning the base placement, our solution can enable operators to flexibly balance the tradeoff between satisfaction and fairness of resources sharing in service platforms.

Server Resource Dimensioning and Routing of Service Function Chain in NFV Network Architectures

The Network Function Virtualization (NFV) technology aims at virtualizing the network service with the execution of the single service components in Virtual Machines activated on Commercial-off-the-shelf (COTS) servers. Any service is represented by the Service Function Chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF instances (VNFI) that in general are software components executed on Virtual Machines. In this paper we cope with the routing and resource dimensioning problem in NFV architectures. We formulate the optimization problem and due to its NP-hard complexity, heuristics are proposed for both cases of offline and online traffic demand. We show how the heuristics works correctly by guaranteeing a uniform occupancy of the server processing capacity and the network link bandwidth. A consolidation algorithm for the power consumption minimization is also proposed. The application of the consolidation algorithm allows for a high power consumption saving that however is to be paid with an increase in SFC blocking probability.