Virtual Network Services Research Articles

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(i)</i> to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ii)</i> to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization–management and orchestration (NFV–MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(iii)</i> to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.

Efficient solutions to the placement and chaining problem of User Plane Functions in 5G networks

This study attempts to solve the placement and chaining problem of 5G User Plane Functions (UPFs) in a Multi-access Edge Computing (MEC) ecosystem. The problem is formalized as a multi-objective Integer Linear Programming (ILP) model targeted at optimizing provisioning costs and quality of service. Our model takes into account several aspects of the system such as UPF-specific considerations, the Service Function Chain (SFC) requests topology (single and multiple branches), Virtual Network Function (VNF) order constraints, service demands, and physical network capacities. Since the formulated problem is NP-hard, two heuristic solutions are devised to enhance solution efficiency. Specifically, an algorithm called Priority and Cautious-UPF Placement and Chaining (PC-UPC) and a simulated annealing (SA) meta-heuristic are proposed. Through extensive simulation experiments, we evaluated the performance of the proposed solutions. The results revealed that our solutions outperformed the baselines (i.e., two greedy-based heuristics and a variant of the classical SA) and that we had obtained nearly optimal solutions with significant reductions in running time. Moreover, the PC-UPC algorithm can effectively avoid SFC rejections and improve provisioning costs by considering session requirements, current network conditions, and the effects of VNF mapping decisions. Additionally, the proposed SA approach incorporates several mechanisms (e.g., variable Markov chain length and restart–stop) that allow the improvement of not only the quality of the solutions but also their computation time.

Multi-attribute-based QoS-aware virtual network function placement and service chaining algorithms in smart cities

Mobile Edge Cloud (MEC) and Network Function Virtualization (NFV) have been emerging as promising technologies on providing low-latency IoT services in smart cities. However, the MEC can only provide limited number of IoT services because of its finite computing resource. How to deploy VNF instances and route data packets for IoT services in the MEC has been considered as a nontrivial problem. The problem becomes more complicated when taking Quality of Service (QoS) into consideration. In this paper, we study the VNF placement and service chaining problems while considering multi-attribute-based QoS in smart cities. We first formulate the problem as an Integer Linear Programming (ILP) and utilize randomized rounding technique to calculate a solution to the problem. Considering IoT services have different QoS requirements, we design an unsplittable flow based heuristic algorithm to deploy computing resource for each type of IoT service. Finally, large quantities of simulations prove that our algorithms outperform their counterparts.

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.

Multi-tier multi-tenant network slicing: A multi-domain games approach

The 5G slice networks will play a critical role in meeting the stringent quality-of-service requirements of different use cases, reducing the Capital Expenditure (CapEX) and Operational Expenditure (OpEX) of mobile network operators. Owing to the flexibility and ability of 5G slice networks to meet the needs of different verticals, it attracts new network players and entities to the mobile network ecosystem, and therefore it creates new business models and structures. Motivated by this development, this paper addresses the dynamic resource allocation in a multi-slice multi-tier multi-domain network with different network players. The dynamic resource allocation problem is formulated as a maximum utility optimisation problem from a multiplayer multi-domain perspective. Furthermore, a 3-level hierarchical business model comprising Infrastructure Providers (InPs), Mobile Virtual Network Operators (MVNOs), Service Providers (SPs), and slice users are investigated. We propose two schemes: a multi-tier multi-domain slice user matching game scheme and a distributed backtracking multiplayer multi-domain game scheme in solving the transformed maximum utility optimisation problem. We compare the multi-tier multi-tenant multi-domain game scheme with a Genetic Algorithm (GA) Intelligent Latency-Aware Resource (GI-LARE) allocation scheme, and a static slicing resource allocation scheme via Monte Carlo simulation. Our findings reveal that the proposed scheme significantly outperforms these other schemes.

DAVINCI: online and Dynamic Adaptation of eVolvable vIrtual Network services over Cloud Infrastructures

Service function chains, or more generally, virtual network services, evolve throughout their life-cycle due to traffic fluctuations, resource usage, and the stringent requirements of the SLO (Service Level Objective) that must be fulfilled. Network operators resort to elasticity mechanisms (e.g., vertical and horizontal scaling) and migration procedures to adapt the service function chains dynamically to meet their requirements constantly. However, most state-of-the-art solutions do not evaluate the overall impact of such mechanisms on the service function chains or the resulting penalty costs (e.g., migration downtime and SLO violation). To bridge this gap, we propose DAVINCI, a decision-making tool with different adaptation policies that allows the network operator to adapt the service function chains to minimize network changes (e.g., reallocating paths and VNFs) while keeping penalty costs at their lowest level. Moreover, DAVINCI allows for the adaptations (e.g., migration, vertical and horizontal scaling) to be expressed as a set of decisions and leverages the Min Cost Flow problem to estimate migration downtime. The analytical evaluation shows that DAVINCI outperforms the existing state-of-the-art solution (NFV-PEAR) in terms of migration, traffic costs, and the overhead induced by the migration and elasticity mechanisms while significantly reducing penalty costs.

Enhancing Security and Privacy in Local Area Network (LAN) with TORVPN Using Raspberry Pi as Access Point: A Design and Implementation

Network security is designed to protect the integrity, confidentiality and accessibility of computer networks and data using both software and hardware technologies. Every person, including organization requires a degree of network security solutions in place to protect it from the ever-growing landscape of cyber threats in the wild today. This paper aims to design and implement TorVPN as an access point using Raspberry Pi in enhancing security and privacy in Local Area Network (LAN). This access point was implemented by using the combination of The Onion Router (Tor) and Virtual Private Network (VPN) services. Then, the graphical user interface application was developed to assist and facilitate the user to access the network securely without having the difficulties to configure and install any software. To determine the effectiveness of the proposed work, there were two experiments involved in this study. Firstly, the confidentiality test which to verify its privacy in keeping the information securely. Secondly, the performance test of the Internet connectivity in terms of ping, download and upload speed. The encourage results was expected as the confidentiality tested on the TorVPN access point network has shown the positive outcome by securing client’s Internet data packet. While, the Internet connectivity is not stable enough, when the client’s IP address changed in the network. Hence, the combination VPN and Tor service using Raspberry Pi as an access point inside the LAN is suitable and secure the information in the network, but do not suggest for user who wants a good Internet connectivity.

A Survey of Virtual Private LAN Services (VPLS): Past, Present and Future

Virtual Private LAN services (VPLS) is a Layer 2 Virtual Private Network (L2VPN) service that has gained immense popularity due to a number of its features, such as protocol independence, multipoint-to-multipoint mesh connectivity, robust security, low operational cost (in terms of optimal resource utilization), and high scalability. In addition to the traditional VPLS architectures, novel VPLS solutions have been designed leveraging new emerging paradigms, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), to keep up with the increasing demand. These emerging solutions help in enhancing scalability, strengthening security, and optimizing resource utilization. This paper aims to conduct an in-depth survey of various VPLS architectures and highlight different characteristics through insightful comparisons. Moreover, the article discusses numerous technical aspects such as security, scalability, compatibility, tunnel management, operational issues, and complexity, along with the lessons learned. Finally, the paper outlines future research directions related to VPLS. To the best of our knowledge, this paper is the first to furnish a detailed survey of VPLS.

Customizable Deployment of NFV Services

Network Functions Virtualization (NFV) promotes a paradigm shift in the core network, by enabling the execution of network functions on a virtualized software plane instead of on dedicated hardware. Despite its benefits, NFV introduces new challenges, of which we highlight those related to the deployment of virtualized network services. Current NFV deployment solutions (i.e., those for composition, embedding, and scheduling) are usually limited to optimize hard-coded criteria, and cannot be customized to address specific demands defined by both network operators and NFV-as-a-Service customers. In this paper, we present a customizable NFV deployment framework that allows multiple criteria and multiple objectives to be applied to service composition, embedding, and scheduling. We evaluate the proposed framework integrated to deployment solutions specified in the literature. A case study is presented for the customized deployment of a traffic control and security service, and demonstrates the flexibility and effectiveness of the proposed framework.

Resource-Ability Assisted Service Function Chain Embedding and Scheduling for 6G Networks With Virtualization

While 5G is being deployed all around the world, the industry and academia start the investigation of 6G. Network function virtualization (NFV) is seen as the key enabler towards the flexible resource management and sharing of 6G networks. The main idea of NFV is to decouple the physical devices from the specific functions that run on them. In NFV, virtual network service (NS) is modeled as a service function chain (SFC). The challenges in SFC description, composition, embedding, and scheduling are key issues in NFV. In this paper, we focus on the joint SFC embedding and scheduling for NFV in 6G networks. With the assistance of sensing the physical node resource abilities, we propose a novel SFC embedding and scheduling algorithm, which is named as RA-SFC-6G . A new service is first composed as an SFC which is an ordered sequence of virtual network functions (VNFs). Afterwards, our RA-SFC-6G algorithm will select the physical nodes with stronger resource abilities and abundant resources to accommodate the SFC. In addition, the NS is guaranteed to be implemented without violating the NS maximum allowed scheduling time. If violated, our RA-SFC-6G can re-embed and re-schedule the violated VNFs to suitable positions. In order to validate our RA-SFC-6G performance, we do the experiment work. Experiment results show that our RA-SFC-6G achieves at least 10 $\%$ higher SFC acceptance ratio than the previous counterparts and the typical heuristics.

Fair Scheduling and Rate Control for Service Function Chain in NFV Enabled Data Center

With the emerging paradigm of Virtual Network and Network Function Virtualization (NFV), the data center operator can flexibly manage the network to reduce Operating Expenditures (OPEX) and Capital Expenditures (CAPEX). Some new issues including the assignment of Virtual Network Functions (VNFs) and Service Function Chain (SFC) scheduling should be considered to apply in the communication. Mathematically, the SFC scheduling process can be formulated as a queuing system, and every server installed with VNF instances can be regarded as a service node, where batched requests are parallel submitted and processed one after another. In the management of SFC, the cloud service provider needs to resolve the problem of network congestion. Latency and throughput are two important but contradictory indexes in the network management, and both of them are deserved to be optimized during the scheduling of SFC requests. In this paper, different from most existing studies, we focus on the service rate control problem in the scheduling of SFC requests. Firstly, we formulate this problem as an integer programming problem. Through a cooperative game approach, a Nash bargaining based model is proposed to jointly optimize the latency and throughput, which is proven to provide fair performance guarantees by both theoretical analysis and simulation. To improve the scalability of the proposed algorithm, we also design a polynomial two-phase heuristic to perform Pareto optimization. Simulation evaluation shows that the proposed algorithm can implement balanced traffic scheduling and avoid excessive server latency caused by network congestion.

Robust Service Provisioning With Service Function Chain Requirements in Mobile Edge Computing

With the advent of Network Function Virtualization (NFV) technology, more and more mobile users make use of virtual network services in Mobile Edge Computing (MEC) networks. Each service request not only requests for a service but also a Service Function Chain (SFC) associated with the request. How to effectively allocate resources in MEC to meet the resource demands of user service requests to maximize the expected profit of the network service provider poses a great challenge. Most existing studies considered resource allocation and scheduling in MEC for user request admissions, under the assumption that the amounts of different resources demanded by each request are given a prior and do not change during the execution of the request. In practice, the resource demands during the implementation of a request are dynamically evolving. This uncertainty of resource demands at different execution stages of the request does impact the service quality and the profit of network service providers. Thus, providing robust services to users against their resource demand uncertainties is a critical issue. In this paper, we study the robust service function chain placement (RSFCP) problem under the uncertainty assumption of both computing resource and data rates demanded by request executions, through the placement of SFCs. We first formulate the RSFCP problem as a Quadratic Integer Programming (QIP) and show that the problem is NP-hard. We then develop a near-optimal approximation algorithm for it, by adopting the Markov approximation technique. We also analyze the proposed approximation algorithm with the optimality gap, and the bounds on the convergence time and perturbation caused by resource demand uncertainties. We finally evaluate the performance of the proposed algorithm through analytical and empirical analyses. Experimental results demonstrate that the proposed algorithm is promising, compared with existing baseline algorithms.

Co-creation or Co-destruction: A Perspective of Online Customer Engagement Valence.

The increasing interest in online shopping in recent years has increased the importance of understanding customer engagement valence (CEV) in a virtual service network. There is yet a comprehensive explanation of the CEV concept, particularly its impact on multi-actor networks such as web stores. Therefore, this study aims to fill this research gap. In this study, past literature in the marketing and consumer psychology field was critically reviewed to understand the concept of CEV in online shopping, and the propositional-based style was employed to conceptualize the CEV within the online shopping (web stores) context. The outcomes demonstrate that the valence of customer engagement is dependent on the cognitive interpretation of signals that are prompted by multiple actors on a web store service network. If the signals are positively interpreted, positive outcomes such as service co-creation are expected, but if they are negatively interpreted, negative outcomes such as service co-destruction are predicted. These notions create avenues for future empirical research and practical implications.

OVS-CAB: Efficient rule-caching for Open vSwitch hardware offloading

Open vSwitch (OVS) is a crucial component in today’s Software Defined Networking (SDN) and Network Function Virtualization (NFV) ecosystem. It has been widely deployed in datacenters to provide connectivity and virtual network services. Fast growing demands for cloud services require OVS to support complicated rules and provide high-throughput and low-latency data exchange with low host CPU overhead. However, the sophisticated packet classification operation required for every packet makes this goal a significant challenge. Hardware offloading is a popular solution boosting OVS’s performance, but the significant memory usage when directly applying the OVS’s rule-caching scheme makes it infeasible. In this paper, we propose OVS-CAB, an efficient rule-caching for OVS hardware offloading.OVS-CAB can handle data traffic at a high line rate, while keeping host CPU usage low by offloading rule lookup functions to a hardware-based Smart Network Interface Card (Smart-NIC). Moreover, OVS-CAB achieves a high data plane hit rate and keeps hardware memory space usage low by intelligently caching selected rules. This rule-caching algorithm between the OVS control plane and hardware data plane is based on a previously proposed rule-caching scheme, CAching in Buckets (CAB). This paper includes the rule-caching design and a detailed implementation of the update and timeout operations for cached rules. For implementation and evaluation, we prototype OVS-CAB on a Netronome Smart-NIC using the P4 programming language. Both simulated and experimental results show that OVS-CAB significantly boosts the throughput of OVS and further reduces the host CPU utilization by 97%. Compared to other OVS hardware offloading solutions, OVS-CAB increases the rule hit rate in the data plane dramatically, while keeping memory usage at a very low level.

VPN Services between a Rock and a Hard Place: The Freedome Case

Providers of commercial Virtual Private Network services are expected to protect the privacy of their customers, but also to co-operate with law enforcement in legitimate criminal investigations in accordance with the law. In a case concerning a commercial VPN service, the seizure of VPN user logs was challenged in court by the service provider, a Finnish cybersecurity company. Drawing on this case and the reasoning adopted by the national courts, this paper explores the dual role of VPN service providers and the fine line between subscriber data and traffic data in Finnish law and on the European level. Ultimately, it is argued that uncontrolled and unlimited law enforcement access to VPN user logs would jeopardize privacy rights and the business interests of legitimate VPN providers, and that it might additionally lead to undesirable consequences for law enforcement interests.

Physical impairments awareness based virtual network mapping strategy of elastic optical networks

As virtual networks services emerge increasingly with higher requirement of flexibility and robust, great complex challenges caused by physical-layer impairments are presented to the elastic optical networks (EON). Aimed to solve this problem, this paper proposes a physical impairment awareness based virtual network mapping stragegy of EON. The physical impairments awareness model is established, including both of linear factors and nonlinear ones. On this basis, this paper proposes a virtual network mapping strategy with detailed procedures, combined with node importance factors during the virtual network mapping procedure. Test results show that the proposed approach is able to reduce blocking rate and enhance services supporting ability of EON.

IoT Ecosystem on Exploiting Dynamic VNF Orchestration and Service Chaining: AI to the Rescue?

An efficient automated virtual network function (VNF) deployment and service function chaining (SFC) can induce a significant improvement in the overall performance of various IoT services. Few concerns regarding the latency benefits, energy consumption expenditure, and migration costs are required to be taken into consideration collaboratively for the solution method to accommodate supreme privileges for both users and providers. However, most of the works existing in the literature emphasize these issues exclusively. Additionally, they focus on employing traditional mathematical programming-based approaches to find optimal solutions that are computationally expensive. Thus, state-of-the-art methods are infeasible and not prompt enough to provide real-time solutions for massive IoT services. In this article, we propose the utilization of different deep learning and reinforcement learning techniques (e.g., artificial neural networks, convolutional neural networks, deep Q-networks, and federated learning) for swift VNF orchestration and SFC. Moreover, we identify some challenges and their potential solutions associated with these sophisticated learning models. Then we present some simulation results on a VNF deployment case study demonstrating that deep learning techniques can be a significant breakthrough with promising potential to resolve most of the mentioned concerns incorporated with the VNF orchestration and SFC generation problem.

Grouping Service Chains of Multiple Flows in NFV-Based Networks

In Software Defined Networks (SDNs), flows usually request to be processed by a service chain (an ordered set of virtualized network services). SDN-enabled networks manage the routing and processing of flows by a large number of associated finer-granularity rules. These rules are maintained by switches in their local hardware such as Ternary Content Addressable Memories (TCAMs), which support high-speed parallel lookup on wildcard patterns. However, the capacity of hardware switches is limited to thousands because of their high requirements for cost and power. In order to avoid much slower matching by using software switches or even packet loss, we can group flows so that all matching rules can be placed in hardware switches. In such a grouping, all flows in each group match only one rule and will be forwarded to the same routing path, instead of each flow matching one rule. This will result in a longer delay because of processing by the longer newly-grouped service chain. In this paper, we efficiently group flows to minimize the total cost while satisfying the capacity constraint of the forwarding tables in hardware switches. We first prove the submodularity of our objective function and propose a corresponding performance-guaranteed solution. Additionally, we design an efficient heuristic solution based on the classic k-means algorithm. Furthermore, we include discussions on dynamic network situations (insertion, deletion, and update of flows) and an alternative objective. We also conduct real experiments on our testbed to indicate the practicality of our motivation. Extensive simulations are conducted to evaluate the performance of our proposed algorithms.

Necklace: An Architecture for Distributed and Robust Service Function Chains With Guarantees

The service function chaining paradigm links ordered service functions via network virtualization, in support of applications with severe network constraints. To provide wide-area (federated) virtual network services, a distributed architecture should orchestrate cooperating or competing processes to generate and maintain virtual paths hosting service function chains while, guaranteeing performance and fast asynchronous consensus even in the presence of failures. To this end, we propose a prototype of an architecture for robust service function chain instantiation with convergence and performance guarantees. To instantiate a service chain, our system uses a fully distributed asynchronous consensus mechanism that has bounds on convergence time and leads to a (1 $-\,\,1/ {e}$ )-approximation ratio with respect to the Pareto optimal chain instantiation, even in the presence of (non-byzantine) failures. Moreover, we show that a better optimal chain approximation cannot exist. To establish the practicality of our approach, we evaluate the system performance, policy tradeoffs, and overhead via simulations and through a prototype implementation. We then describe our extensible management object model and compare our asynchronous consensus’s overhead against Raft, a recent decentralized consensus protocol, showing superior performance. We furthermore discuss a new management object model for distributed service function chain instantiation.

Copyright issues related to reproduction rights arising from streaming

AbstractWith the rapid development of streaming media technology, more and more people have begun to watch films on streaming websites. However, it has been noted that some of them are streaming unauthorized content, which has posed a major challenge to the entertainment sector's traditional business model. Against this background, legal issues arising from the streaming of media online is an area of huge current interest, one issue being the exact liability of providers and users of streaming services and whether they are infringing on the reproduction right of the copyright holders. Since the liability of platforms has been extensively discussed in the academic literature, in this article we mainly focus on the liability of users who stream unauthorized works. To this end, the article analyses the reproduction act of streaming platform users under the UK and European Union approaches and examines Article 5(1) of the Information Society Directive in the context of streaming. In addition, we explore the liability of users who stream unauthorized content via virtual private network services.