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The network function virtualization (NFV) uses virtualization technology to separate software from hardware. One of the most important challenges of NFV is the resource management of virtual network functions (VNFs). According to the dynamic nature of NFV, the resource allocation of VNFs must be changed to adapt to the variations of incoming network traffic. However, the significant delay may be happened because of the reallocation of resources. In order to balance the performance between delay and quality of service, this paper firstly made a compromise between VNF migration and energy consumption. Then, the long short-term memory (LSTM) was utilized to forecast network traffic. Also, the asymmetric loss function for LSTM (LO-LSTM) was proposed to increase the predicted value to a certain extent. Finally, an experiment was conducted to evaluate the performance of LO-LSTM. The results demonstrated that the proposed LO-LSTM can not only reduce migration times, but also make the energy consumption increment within an acceptable range. [ABSTRACT FROM AUTHOR]

The development of Fifth-Generation (5G) mobile communication technology has remarkably promoted the spread of the Internet of Things (IoT) applications. As a promising paradigm for IoT, edge computing can process the amount of data generated by mobile intelligent devices in less time response. Network Function Virtualization (NFV) that decouples network functions from dedicated hardware is an important architecture to implement edge computing, deploying heterogeneous Virtual Network Functions (VNF) (such as computer vision, natural language processing, intelligent control, etc.) on the edge service nodes. With the NFV MANO (Management and Orchestration) framework, a Service Function Chain (SFC) that contains a set of ordered VNFs can be constructed and placed in the network to offer a customized network service. However, the procedure of NFV orchestration faces a technical challenge in minimizing the network cost of VNF placement due to the complexity of the changing effect of traffic volume and the dependency on the VNF relationship. To this end, we jointly optimize SFC design and VNF placement to minimize resource cost while taking account of VNF dependency and traffic volume scaling. First, the problem is formulated as an Integer Linear Programming (ILP) model and proved NP-hard by reduction from Hamiltonian Cycle problem. Then we proposed an efficient heuristic algorithm called Traffic Aware and Interdependent VNF Placement (TAIVP) to solve the problem. Compared with the benchmark algorithms, emulation results show that our algorithm can reduce network cost by 10.2% and increase service request acceptance rate by 7.6% on average. [ABSTRACT FROM AUTHOR]

Virtual resource load prediction in network function virtualization (NFV) is the subject of intense research due to its crucial role in enabling proactive resource adaptation in dynamic NFV environments whose resource demand constantly changes. Several long short‐term memory (LSTM)‐based approaches have been proposed to forecast the resource load of multiple resource attributes of a virtual network function (VNF) in a service function chain (SFC). In this article, we present NFVLearn, a flexible multivariate, many‐to‐many LSTM‐based model which uses different types of resource load history (CPU, memory, I/O bandwidth) from various VNFs of an SFC to predict future loads of multiple resources of a VNF. We then compare four novel automated input selection frameworks for NFVLearn. Simulations on those frameworks based on graph neural networks, Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall rank correlation coefficient demonstrate that models using lesser, highly correlated input features retain high prediction root mean squared error accuracy and coefficients of determination scores by leveraging resource attribute inter‐dependencies from the SFC. Those results show that resource attribute interdependency‐based input feature selection frameworks can reduce overhead in the control plane while keeping high accuracy and high fidelity resource load prediction of multiple resource attributes. [ABSTRACT FROM AUTHOR]

The network function virtualization (NFV) feature lies at the core of modern networking, and it allows on-demand real-time integration of new network functions, which is a great benefit for large-scale infrastructure networks. In contrast to the functional benefits, NFV introduces software complexity and computational overhead through additional abstraction layers. The current article addresses the function validation problem in large-scale infrastructure networks of Internet Service Providers (ISPs) and proposes the utilization of blockchain as a validation technology, as opposed to implementing a custom validation solution. The current work showcases a practical architecture implementation to address the service validation in service provider large-scale networks. The POX-based solution to control software-defined networks (SDN) for NFV is extended to offer additional blockchain capabilities. Thus, a blockchain node is integrated and executed in the POX SDN controller. Transaction experiments are performed between two endpoints located in remote locations on the Internet, and the detailed results are presented to validate the utilization of the blockchain technology used on SDNs’ control plane.

The emergence of new network services such as immersive cloud XR and holographic communication puts forward higher requirements for network service quality.To ensure the availability of network services, the network service delay and reliability must be controlled within a certain quality of service according to the attributes and dependencies of network functions.However, the traditional network representation forms, such as bitmap and matrix, cannot cover these key network information, resulting in the information loss in the input stage of the algorithm, which leads to the deviation of the calculation results.Therefore, in order to accurately extract user needs and reflect the dynamic changes of network resources, knowledge graph was adopted to represent the network and its services, an online placement algorithm of service function chain based on knowledge graph was proposed.Based on this, a relationship alignment method based on editing distance was designed to guide the online placement of service function chains under complex dependency relationships.Experimental results show that the proposed algorithm can improve the placement accuracy of service function chain by 10%~15% and reduce the average network energy consumption by about 13%.The proposed algorithm has low complexity and high timeliness.

The traditional network security architecture ensures network security by directing traffic through hardware based network security function devices.Since the architecture consists of fixed hardware devices, it leads to a single form of network security area deployment and poor scalability.Besides, the architecture cannot be flexibly adjusted when facing network security events, making it difficult to meet the security needs of future networks.The intelligent service chain system for network security resource pool was based on software-defined network and network function virtualization technologies, which can effectively solve the above problems.Network security functions of virtual form were added based on network function virtualization technology, combined with the existing hardware network elements to build a network security resource pool.In addition, the switching equipment connected to the network security elements can be flexibly controlled based on software-defined network technology.Then a dynamically adjustable network security service chain was built.Network security events were detected based on security log detection and a expert library consisting of security rules.This enabled dynamic and intelligent regulation of the service chain by means of centralized control in the face of network security events.The deployment process of the service chain was mathematically modeled and a heuristic algorithm was designed to realize the optimal deployment of the service chain.By building a prototype system and conducting experiments, the results show that the designed system can detect security events in seconds and automatically adjust the security service chain in minutes when facing security events, and the designed heuristic algorithm can reduce the occupation of virtual resources by 65%.The proposed system is expected to be applied to the network security area at the exit of the campus and data center network, simplifying the operation and maintenance of this area and improving the deployment flexibility of this area.

Complex networks are practical tools for modeling, studying, and analyzing complex interactions between objects. These tools are essential in understanding applications, end-users, interactions between compute nodes, and their behaviors in computer networks. Computer networks are undergoing significant expansion due to the proliferation of network devices and compute nodes. One of the main challenges in computer networks is categorizing these compute nodes into clusters of connected compute nodes within these large-scale structures sharing similar features (e.g., Central Processing Unit, memory, disk storage). This paper proposes a set of novel, dynamic, and proactive topology-aware clustering approaches, namely, an Integer Linear Program, chemical reaction optimization, and a game theory approach that leverages the Irving algorithm, originally proposed to solve the stable roommate problem, to form clusters based on the compute nodes' features and their topological structures. Our proposed techniques are suggested to reduce the search space concerning Network Function Virtualization, Cloud-based Networks deployment, and to build on-demand clusters to meet their requirements. In this regard, the solutions aim to help decision-makers facing issues related to scalability and computational complexities of their mechanisms to deploy their cloud-based services effectively. Experimental results demonstrate the proposed approaches' effectiveness and suitability, given their polynomial-time complexities, making them easy to integrate into cloud providers' orchestration systems compared to K-Means and Density-based spatial clustering of applications with noise. [ABSTRACT FROM AUTHOR]

As a novel network architecture, network function virtualization greatly improves the flexibility of service deployment but also increases the complexity of network function virtualization resource allocation (NFV-RA). Many scholars have studied the above problems and put forward corresponding schemes. These schemes are often individually designed and are difficult to compare. A general and efficient simulation platform for the NFV-RA problem is conducive to integration, comparison and analysis of these schemes and speeds up research progress. This paper presents a general simulation platform SFCSim for NFV-RA. SFCSim adopts the discrete-time event scheduling engine, which can support the simulation of static and dynamic deployment of service function chain, service migration, mobility management and other scenarios. Finally, a load-balancing shortest path and three heuristic embedding algorithms are implemented in the platform for the service function chain embedding problem can be used as the benchmark algorithm, and SFCSim is fully compared with the Alevin to verify the performance of the platform [ABSTRACT FROM AUTHOR]

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The network function virtualization (NFV) paradigm replaces hardware‐dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility. For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof‐of‐concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) is known for its ability to reduce deployment costs and improve the flexibility and scalability of network functions. Due to processing capacity limitations, the infrastructure provider may need to instantiate multiple instances of the same network function. However, most of network functions are stateful, meaning that the instances of the same function need to keep a common state and hence the need for synchronization among them. In this paper, we address this problem with the goal of identifying the optimal synchronization pattern between the instances in order to minimize the synchronization costs and delay. We propose a novel network function named Synchronization Function able to carry out data collection and further minimize these costs. We first mathematically model this problem as an integer linear program that finds the optimal synchronization pattern and the optimal placement and number of synchronization functions that minimize synchronization costs and ensure a bounded synchronization delay. We also put forward three greedy algorithms to cope with large‐scale scenarios of the problem, and we explore the possibility to migrate network function instances to further reduce costs. Extensive simulations show that the proposed algorithms efficiently find near‐optimal solutions with minimal computation time and provide better results compared to existing solutions. [ABSTRACT FROM AUTHOR]

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This paper proposes and implements a Network Function Virtualization (NFV) security architecture to provide automatic and efficient detection and mitigation against Web application malware. The mitigation is given by dynamically chaining a Virtual Security Function (VSF) to the data stream to block malicious exploitation traffic without affecting the benign traffic. We implement an NFV Security Controller (NFV-SC) that interacts with an Intrusion Detection System and a Web Application Firewall (WAF), both implemented as VSFs. We also implement a vulnerability scanner and a mechanism to automatically create rules in advance in the WAF-VSF when a security vulnerability is found in an application, even if no malicious traffic has attempted to exploit the flaw. In addition, it dynamically identifies and removes no longer used security rules to improve performance. We implement and evaluate our security proposal in the Open Platform for NFV (OPNFV). The evaluation results in our experimental scenarios show that the NFV security architecture automatically blocks 99.12% of the HTTP malicious traffic without affecting 93.6% of the benign HTTP requests. Finally, we show that the number of rules in the WAF-VSF severely affects the latency to load HTTP response headers and that the number of redirection OpenFlow rules within Open vSwitches is not enough to significantly impact the end-user experience in modern web browser applications. [ABSTRACT FROM AUTHOR]

Software defined network (SDN) and network function virtualization (NFV) are new paradigms and technologies of the network which support the best experience of providing functions and services, managing network traffic, and a new way of control. They support virtualization and separating data from control in network devices, as well as provide services in a software-based environment. Internet of things (IoT) is a heterogeneous network with a massive number of connected devices and objects. IoT should be integrated with such technologies for the purpose of providing the capabilities of dynamic reconfiguration with a high level of integration. This paper proposes a conceptual architecture for integrating SDN and NFV with IoT. The proposed work combines the three technologies together in one architecture. It also presents the previous works in this area and takes a look at the theoretical background of those technologies in order to give a complete view of proposed work. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digitalization is one of the factors that affects the acceleration of the application of telecommunications technologies such as 5G. The 5G technology that has been developed today does not yet meet different performance and manageability standards, particularly for data center networks as a supportive technology. Software-defined networking (SDN) and network function virtualization (NFV) are two complementary technologies that are currently used by almost all data centers in the telecommunications industry to rectify performance and manageability issues. In this study, we deliver an integrated SDN-NFV architecture to simplify network management activities in telecommunication companies. To improve network performance at the computing level, we performed a modification of a networking system at the computing level, underlying NFV devices by replacing the default virtual switch with a data plane development kit (DPDK) and single root I/O virtualization (SR-IOV). This study evaluated the proposed architecture design in terms of network performance and manageability. Based on 30 days of observation in prime time, the proposed solution increased throughput up to 200 Mbps for the server leaf and 1.6 Gbps for the border leaf compared to the legacy architecture. Meanwhile, the latency decreased to 12 ms for the server leaf and 17 ms for the border leaf. For manageability, we tested three different scenarios and achieved savings of 13 min for Scenario 1, 22 min for Scenario 2 and 9 min for Scenario 3. [ABSTRACT FROM AUTHOR]

Future train control systems are expected to support high speed railways, to be automated and intelligent. European Railway Traffic Management System (ERTSM) and European Train Control Systems (ETCS) provide a standard aimed at optimization of driving, constant control on the effect of actions taken for train safety and activating of train braking in case of emergency. Radio Block Center (RBC) is a central control in the ETCS ground topology and is responsible for the security of each train in its area. Currently, the RBC is proprietary equipment with monolithic design. In this paper, we propose a new disaggregated open architecture of RBC which enables embedding intelligence. The proposed architecture of intelligent RBC is based on separation of nonreal time functions and near real time functions. A use case illustrates the approach applicability. Some implementation issues are discussed.

As the key support for the emergence of cluster combat eff ectiveness, the design architecture of aviation information network will infl uence the combat mode in the aviation fi eld.Based on the future operational requirements of aviation cluster, the defi ciency of traditional aviation information network was introduced and the technical characteristics of SDN/NFV was analyzed.On this basis, combined SDN/NFV technology and aviation information network, an aviation information network architecture based on SDN/NFV was proposed, and its components were briefl y described.The future research directions and challenges of aviation information network architecture based on SDN/NFV were summarized.It also explored the ability of establishing effi cient information exchange and fi ne-grained aggregation of resources in aviation information network, which provided reference for the development of aviation information network in the future.

This paper tackles the problem of optimum Virtual Machine placement, focusing on an industrial use-case dealing with capacity planning for Virtualized Network Functions. The work is framed within an industrial collaboration with the Vodafone network operator, where a particularly important problem is the one of optimum deployment of Softwarized Network Functions within their Virtualized Networking Infrastructure, spanning across several EU countries. The problem is particularly difficult due to the presence of a multitude of placement constraints that are needed in the industrial use-case, including soft affinity constraints, that should be respected only as secondary objective; furthermore, in some EU regions, the size of the problem makes it unfeasible to solve it with traditional MILP-based techniques. In this work, we review and address limitations of previously proposed heuristics for this kind of problems, and propose a new placement strategy that is shown experimentally to be more effective in dealing with soft affinity constraints. The paper includes an extensive experimental evaluation encompassing a multitude of optimization strategies, applied to a set of problems including both real-world problems that we made available as an open data-set, and additional randomly generated problems mimicking the structure of the original real-world problems. [ABSTRACT FROM AUTHOR]

Modern distributed systems are designed to manage overload conditions, by throttling the traffic in excess that cannot be served through overload control techniques. However, the adoption of large-scale NoSQL datastores make systems vulnerable to unbalanced overloads , where specific datastore nodes are overloaded because of hot-spot resources and hogs. In this paper, we propose DRACO, a novel overload control solution that is aware of data dependencies between the application and the datastore tiers. DRACO performs selective admission control of application requests, by only dropping the ones that map to resources on overloaded datastore nodes, while achieving high resource utilization on non-overloaded datastore nodes. We evaluate DRACO on two case studies with high availability and performance requirements, a virtualized IP Multimedia Subsystem and a distributed fileserver. Results show that the solution can achieve high performance and resource utilization even under extreme overload conditions, up to 100x the engineered capacity. • Overload control is a challenging problem in multi-tier systems with large-scale distributed datastores. • Data dependencies between the application and the distributed datastore tiers cause unbalanced overloads and hot-spots. • Unbalanced overloads in high-scale architectures dramatically affect the availability and the quality of service. • Existing overload mitigation strategies are not effective to protect from unbalanced overload in short term. • A data-dependency aware throttling scheme can mitigate the side effects of unbalanced overload. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) has becoming an emerging technology for ensuring the reliability, security and scalability of data flows. The Virtual Network Function (VNF) embedding problem, which tries to minimize the embedding cost and link connection cost toward customers or maximize network throughput for a given set of NFV-enabled requests, has attracted extensive interests recently. However, the existing works always assume the fixed execution order of VNFs, which limits their application. Thus, we investigate the VNF embedding problem without such limitations in this paper. Firstly, we propose a general transformation framework for the NFV-enabled unicast routing problem with arbitrary order of service function chains, and an optimal algorithm is proposed for the unicast VNF embedding problem without delay constraint. Secondly, an efficient algorithm with theoretical guarantee is also proposed for such a problem with delay constraint. Thirdly, the throughput maximization problem where there exists a set of unicast requests with delay constraints is also investigated, and an efficient algorithm is also proposed to maximize the number of admitted requests while the total traffic delivery cost is minimized. Finally, we evaluate the proposed algorithms via extensive simulations, which demonstrates the high efficiency of the proposed algorithms.

Network function virtualization (NFV) utilizes IT virtualization technology to realize the functions of various network devices and realize the decoupling of hardware and software functions. Typically, a service request specifies the virtual network functions (VNFs) it needs and the order between them. A network flow needs to traverse a set of sequential VNFs called a service function chain (SFC). Although SFC can increase the flexibility of service orchestration to meet the needs of different users, network providers face many challenges due to the need to ensure service reliability and some constraints. Therefore, how orchestrating SFC and designing a suitable network architecture is a critical issue in this field at present. We propose an efficient network layer-based SFC orchestration method, called SFC-hierarchical orchestration (SFC-HO). Our method separates virtual and physical networks into layers and computes the availability of resources in each layer. We filter the VNFs of each layer and deploy them to the physical layer that meets the greatest benefit. To this end, we formulate the problem as an integer linear programming (ILP) problem to minimize the total deployment cost. In order to further optimize the layering strategy, we innovatively use the Benders decomposition method to decompose the SFC-HO problem into two sub-problems, which are the hierarchical mapping problem of VNFs and the routing problem between nodes. We propose two algorithms for the two problems respectively. The simulation results show that compared with the SFC orchestration process in the traditional network model. Our research can effectively improve the reliability of the SFC, reduce the delay of the routing process, and effectively reduce the cost consumption. Our research effectively solves the problem of difficult service orchestration for operators in the face of diverse service demands and a large number of service requests.

Bitcoin introduced a cryptocurrency as a form of public ledger consequently that turned into a most popular security technology, Blockchain. Its integrated mining technology lies the key security mechanism. The system allows forming a pool mining group to solve a particular job and share their revenues to their CPU usage while one of them successfully mines a block. To mine a block, a cryptographic puzzle should be solved, which requires significant compute resources that cause huge energy consumption. On the other hand, recent statistics show that low computational energy-restricted Internet of Things (IoT) devices are increasing exponentially. Although it has low energy and limited computation power, it is large in quantity when it is integrated. So we focus on a stochastic geometry theory, which resolves the issue of block mining computation via utilizing multiple mobile IoT devices, given that these IoT devices are Computation Capable Nodes (CCNs). To further normalize this issue, we propose an efficient mathematical solution that uses smart coordination of Virtual Network Functions (VNFs) for IoT devices to enable their CPU usage efficiently. At the same time, the work and credit point distribution policy is smartly handled through virtual pool mining. The proposal renders Network Function Virtualization technology to configure VNF, and Service Function Chain technology is utilized to enable the network flow of such VNFs. New algorithms are presented to solve multiple issues like node discovery, computation offloading, and work credit point distribution. Our goal is to minimize energy consumption within the given time constraint. Implementation results show that although virtual functions for block mining require extensive computations in IoT devices, dividing computation work into small fractions called tasks embedded with VNF, and offloading them to nearby CCNs, tend to minimize the cost and energy consumption of individual shared miners. The overall mining process is proved efficient and faster.

Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.

Cloud network security has become a strong need under the background of the new infrastructure and digital economy, while cloud network integration security has been defined as an ongoing trends.Initially, three major types of problems facing 5G private network users were presented in providing overall security services, and an overall software-defined security architecture in the 5G+ era was proposed.Furthermore, the corresponding prototype system design and implementation was discussed based on this architecture.At the end, the verification shows that the software-defined security orchestration and scheduling system based on the cloud-based architecture helps to accurately solve the overall business security threats and hidden dangers of 5G private network enterprise customers in the 5G+ era, providing systematic reference value for subsequent research.

Network function virtualization redefines a network service as a softwarized chain of virtual network functions (VNFs), which decouples the specific network service from dedicated devices and greatly reduces the hardware cost. The VNFs are generally deployed on common off-the-shelf servers and statistically share computational resources. Due to the inherent integer constraints, the corresponding VNF mapping and scheduling issue is a highly challenging task. In this paper, we consider the mapping and scheduling of VNFs for a given network service, for which a flexible job shop scheduling problem is formulated to optimize the max-min fairness while ensuring the delay requirements of different service chains. Specifically, we propose a deep reinforcement learning method based on offline proximal policy optimization, which dynamically determines the mapping and scheduling decision based on the state of unfinished service chains. The proposed algorithm is scalable to the number of service chains and can be enhanced by Monte Carlo tree search. Numerical results show that the proposed algorithm outperforms the traditional random forest and the greedy algorithms in terms of both service fairness and acceptance ratio. [ABSTRACT FROM AUTHOR]

Network function virtualization provides programmable in-network middlewares by leveraging virtualization technologies and commodity hardware and has gained popularity among all mainstream network device manufacturers. Yet it is challenging to apply coverage-guided fuzzing, one of the state-of-the-art vulnerability discovery approaches, to those virtualized network devices, due to inevitable integrity protection adopted by those devices. In this paper, we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protection bypassing method, which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference technique. We implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols, SNMP , DHCP and NTP , on nine popular virtualized network devices. NDFuzz obtains an average 36% coverage improvement in comparison with its black-box counterparts. NDFuzz discovers 2 0-Day vulnerabilities and 1 1-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of them. All discovered vulnerabilities are confirmed by corresponding vendors. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, the service function chain (SFC) orchestration problem in data centers was investigated. The relationship between request arrival rate, computing resources and processing delay were analized. A flexible resource allocation optimization model aiming at maximize deployment benefits was proposed. Moreover, a heuristic method is developed to solve the problem in two stages. Firstly, the resource usage for SFC was optimized based on flexible resource allocation considering service delay. Secondly, for the virtual network function deployment and link mapping, the top-down and bottom-up search strategies based on worst-fit strategy were alternately adopted to improve the deployment efficiency and reduce the SFC delay. Finally, simulation with 4 and 6 pods in data center was designed to verify the performance of our heuristic algorithm. Experimental results demonstrated that, compared with existing methods, the development profit, success rate and resource utilization were improved in our algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To meet the client's new business requirements or add additional security protection functions, the already hosted network service extension problem in the cloud network based on network function virtualization was researched. The network service extension in the cloud network was modeled as an integer linear programming, considering the impact on initial service, extended graph deployment, resource capacity and virtual network function affinity constraints, and so on. To deal with the computational complexity and dynamism of future large-scale cloud networks, a QGA-based network service extension algorithm was proposed to improve solution efficiency and solution quality through quantum parallel computing. Simulation results prove that the efficient performance of the proposed algorithm in terms of extension successful ratio and average resource costs, and the proposed algorithm has low time complexity. [ABSTRACT FROM AUTHOR]

Since ETSI introduced the architectural framework of network function virtualization (NFV), telecom operators have paid more attention to the synergy of NFV and cloud computing. With the integration of the NFV cloud platform, telecom operators decouple network functions from the dedicated hardware and run virtualized network functions (VNFs) on the cloud. However, virtualization degrades the performance of VNF, resulting in violating the performance requirements of the telecom industry. Most of the existing works were not conducted in a cloud computing environment, and fewer studies focused on the usage of enhanced platform awareness (EPA) features. Furthermore, few works analyze the performance of the service function chain on a practical cloud. This paper facilitates the OpenStack cloud with different EPA features to investigate the performance effects of VNFs on the cloud. A comprehensive test framework is proposed to evaluate the verification of functionality, performance, and application testing. Empirical results show that the cloud system under test fulfills the requirements of service level agreement in Rally Sanity testcases. The throughput of OVS-DPDK is up to 8.2 times as high as that of OVS in the performance test. Meanwhile, the hardware-assisted solution, SR-IOV, achieves the throughput at near the line rate in the end-to-end scenario. For the application test, the successful call rate for the vIMS service is improved by up to 14% while applying the EPA features on the cloud. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) makes the realization of specific network functions no longer depend on inherent hardware by executing virtual network functions (VNFs), but realizes network functions in a more flexible programming manner, thereby reducing the pressure of resource allocation on the underlying network. Service function chain (SFC) is composed of a set of fixed order VNFs. These VNFs need to be deployed on appropriate physical nodes to meet user function requirements, i.e., the placement of SFC. Traditional solutions mostly use mathematical models or heuristic methods, which are not applicable in the context of large-scale networks. Secondly, the existing methods do not integrate intelligent learning algorithms into the service function chain placement (SFCP) problem, which limits the possibility of obtaining better solutions. This paper presents a multi-objective optimization service function chain placement (MOO-SFCP) algorithm based on reinforcement learning (RL). The goal of the algorithm is to optimize the resource allocation mode, including several performance indexes such as underlying resource consumption revenue, revenue cost ratio, VNF acceptance rate and network latency. We model the SFCP as a Markov decision process (MDP), and use a two-layer policy network as an intelligent agent. In the training stage of RL, the agent comprehensively considers the optimization objectives and formulates the optimal physical node mapping strategy for VNF requests. In the test phase, the whole SFCP is completed according to the trained node mapping strategy. Simulation results show that the algorithm proposed in this paper has excellent performance in the aspects of underlying resource allocation revenue, VNF acceptance rate and so on. In addition, we prove that the algorithm has good flexibility by changing the delay constraint. [ABSTRACT FROM AUTHOR]

Even with Network Function Virtualization (NFV), many commodity network servers have spare cycles. Despite that they are small and irregularly occur, spare cycles are fit for deploying short-lived serverless computing functions at the network edge. In this work, we perform detailed analyses of the benefits and limitations of co-locating serverless functions on NFV-ready servers. We propose NEMO, a novel platform that enables efficient serverless edge function deployment in the NFV environment. NEMO can intelligently harvest spare cycles of network functions to warm up the serverless functions and speed up the function invocation in an agile manner. Besides, NEMO can judiciously manage the thread conflict in a resource-limited environment. We build a prototype of NEMO. Our thorough evaluations show that NEMO can harvest up to 41% spare cycles and achieve about 12.5 $\sim$ ∼ 25X performance improvement compared with straightforward co-location. [ABSTRACT FROM AUTHOR]

In order to solve the problems of excessive system cost and long response time of user-oriented service function chain deployment algorithm in mobile edge computing networks, this paper proposed a joint optimization design method for service function chain cost and delay. First, in the construction stage of the service function chain, according to the node location and resource status, the algorithm selected the current best node to reduce the delay between VNFs（virtualized network functions） to improve the response time of the service function chain. Secondly, in the service function chain deployment stage, due to the limited mapping resources, this paper used the node selection algorithm to determine the optimal node mapping order when serving the mapping nodes and selected the shortest weighted path as the communication link between VNFs. The experimental simulation results show that, compared with the existing solutions, the method can effectively reduce the delay and deployment cost, and can significantly improve the success rate of service function chain deployment. [ABSTRACT FROM AUTHOR]

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks. [ABSTRACT FROM AUTHOR]

The past decades have seen a proliferation of middlebox deployment in various scenarios, including backbone networks and cloud networks. Since flows have to traverse specific service function chains (SFCs) for security and performance enhancement, it becomes much complex for SFC routing due to routing loops, traffic dynamics and scalability requirement. The existing SFC routing solutions may consume many resources (e.g., TCAM) on the data plane and lead to massive overhead on the control plane, which decrease the scalability of middlebox networks. Due to SFC requirement and potential routing loops, solutions like traditional default paths (e.g., using ECMP) that are widely used in non-middlebox networks will no longer be feasible. In this paper, we present and implement a scalable and flexible middlebox policy enforcement (SAFE-ME) system to minimize the TCAM usage and control overhead. To this end, we design the smart tag operations for construction of default SFC paths with less TCAM rules in the data plane, and present lightweight SFC routing update with less control overhead for dealing with traffic dynamics in the control plane. We implement our solution and evaluate its performance with experiments on both physical platform (Pica8) and Programming Protocol-independent Packet Processors (P4) based data plane, as well as large-scale simulations. Both experimental and simulation results show that SAFE-ME can greatly improve scalability (e.g., TCAM cost, update delay, and control overhead) in middlebox networks, especially for large-scale clouds. For example, our system can reduce the control traffic overhead by about 85% while achieving almost the similar middlebox load, compared with state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) has been identified to revamp the provisioning of next-generation network services. This new paradigm allows cloud and network/service providers to compose their network services, also known as service function chains (SFCs), in an agile way since the software of the network function is decoupled from the legacy hardware. To reap the benefits of this new technology, there is a need for novel mechanisms that help cloud and network/service providers deploy the increasingly complex virtual network services seamlessly, efficiently, and in a time-efficient way. Existing state-of-the-art techniques often rely on the Integer Linear Programming framework, heuristics/metaheuristics, and greedy methods to deploy the services function chains. However, these techniques although reasonable and acceptable, still suffer from several key limitations: convergence time and scalability. To this end, we propose RAFALE, a suite of solution techniques, to tame this complexity by leveraging the concept of similarity from machine learning and skip-gram modeling framework. To the best of our knowledge, we are the first to tackle these key limitations and propose a suite of solutions to them. RAFALE, a novel approach proposed to find the similarity between the new incoming virtual network service request and all the already-deployed services to learn from the previous experience of deploying techniques and use the same or close similar provisioning techniques. RAFALE is the first and the only method that develops the idea of detecting the similarity between virtual network services. Experimental results show that RAFALE reduces greatly the convergence time needed for provisioning virtual network services and can scale to 100 virtual network functions per virtual network service compared to the state-of-the-art. The Experimental results prove that RAFALE accomplished the NFV promises; decreasing the time and complexity of managing and deploying the virtual services, and providing a solution that is agile, faster, and scalable to deploy the new service requests by skipping one or more service provisioning steps (i.e., detecting and resolving the conflicts among policies, placement, and chaining) while satisfying the validated NFV policies. [ABSTRACT FROM AUTHOR]

The European Telecommunications Standards Institute (ETSI) has developed network function virtualization (NFV) management and orchestration (MANO) for operators to enable quick adaptation of deployment and to reduce the management costs of virtual network functions (VNFs). For scalable and flexible management across transport, cloud, and edge infrastructures, the NFV MANO has been extended into multidomain architectures with network-cloud/edge convergence. In this article, a novel hierarchical distributed overarching architecture of decoupled federation and orchestration frameworks for multidomain ETSI-compliant NFV MANOs is proposed. The proposed architecture can minimize overhead costs for the scalable exchange of abstracted vertical service information through hierarchical brokering and can enable flexible federation through distributed binding among multiple domains. The proposed architecture deploys end-to-end services in single and multiple domains for vertical industries, activates their service processes in a holistic way, and enables operational independence with administrative autonomy. This article serves as an important and necessary step toward the cloud-native design and implementation of federation and orchestration frameworks for multidomain NFV MANOs. [ABSTRACT FROM AUTHOR]