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Network softwarisation introduces flexibility in network management by enabling the deployment of network functions as software modules running on virtual machines. However, this creates new concerns for service availability and reliability due to multiple sources of failures at both software and hardware levels, potentially resulting in service degradations and penalties due to Service Level Agreement (SLA) violations. The survivability of critical services can best be guaranteed by pro-actively provisioning dedicated backup resources for these services, but at the cost of a high resource consumption. Aware of the divergent requirements of future services, a promising alternative is envisaged, allowing non-critical users to use the unused backup resources of high priority users. However, this approach poses a stringent challenge if a critical service disruption occurs, requiring the computation of a traffic rerouting solution for the non-critical requests when preempted from their borrowed resources. In this paper we first propose a generic multi-stage graph based algorithm as an alternative algorithm for Service Function Chain (SFC) deployments. Simulation results demonstrate that the proposed algorithm is optimized in terms of resource utilization, resulting in a 10% improvement in terms of acceptance ratio compared to a given state of the art algorithm, and within a 4% margin of the optimal solution. Based on the mentioned algorithm, we propose a new migration-aware algorithm for the mapping of non-critical services, enabling the noncritical services to borrow the unused backup resources from the critical services while minimizing the probability of preemption they could experience. The migration-aware algorithm results in more than an 8% resource saving, in most scenarios, compared to a dedicated backup strategy, and more than a 70% performance improvement in terms of the number of service preemptions, compared to a cost based algorithm. Additionally, whenever low priority users are preempted from their borrowed resources, we propose a new QoS-aware global-rerouting algorithm for remapping those users, reducing the impact of the service interruption thanks to avoiding the migration of surviving VNFs and virtual links when feasible. The proposed algorithm is shown to outperform a service restoration strategy based on local rerouting, in terms of successful service restoration and resource consumption.

Cloud computing is now known as the most cost-effective platform for delivering big data and artificial intelligence services over the Internet to enterprises and cloud consumers. However, despite many recent security developments, many cloud consumers continue to express great concern about using these platforms because they still have significant vulnerabilities. Typically, Economic Denial of Sustainability (EDoS) attacks exploit the pay-as-you-go billing mechanisms used by cloud service providers, so that a cloud customer is forced to pay an extra fee for the additional resources triggered by the attack activities. In our previous work, we already proposed an system to mitigate such EDoS attacks. Overall, this previous work presented an effective system for detecting abnormal events; however, the false-alarm rates still remain relatively high and detection rates are low, because abnormal events could be caused by the cloud customer. Furthermore, our previous work still consumes a large number of computing resources. Therefore, in this paper, we propose an enhanced scheme to detect and mitigate EDoS attacks efficiently and reliably. Our proposed scheme is composed of online and offline phases, implementing a gated recurrent unit, which not only can capture complex temporal dependence relations in the data, but also can reduce the vanishing gradient problems in time series. First, to reflect the normal patterns, our proposed scheme learns accurate representations of multivariate time series. Next, these representations are used to reconstruct input data. Finally, the reconstruction probabilities not only can be used to find anomalies, but also can provide interpretations. The proposed scheme also introduces a self-adjusting threshold to reduce error rates, whereas existing solutions normally use a hard threshold to analyze the anomalies, which causes increasing error rates. Our comprehensive analysis of the results shows outstanding performance compared to other solutions and our previous work.

Service function chaining (SFC) is an indispensable technique for Internet service providers to efficiently manage their networks. However, SFC poses requirements of additional processing time of service functions (SFs) and increased routing time owing to detoured paths. In this paper, we introduce the use of a programmable data plane (PDP) to reduce the additional processing and routing times in SFC. We first classify the existing PDP-empowered SFC schemes and analyze their pros and cons. An optimization problem, to find the optimal SF embedding strategy minimizing the SFC completion time while efficiently utilizing the PDP switch resources, is formulated and a flow-aware SF embedding (FASE) algorithm that complementarily combines the redundant SF and re-circulation approaches is devised. FASE is implemented over a commercial PDP switch and experimental results demonstrate that FASE can reduce the SFC completion time by up to 33% compared with conventional approaches while utilizing the switch resources efficiently.

Network Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required to follow a sequence of network functions scattered in data center networks. This paper proposes an innovative model and algorithm of traffic engineering for service function chaining (SFC) to maximize cost-efficiency under a delay-guarantee constraint. We first formulate the problem as a mixed-integer linear programming model for obtaining the optimal solution. We then propose an algorithm based on the reinforcement learning principles for finding an approximation solution in a large-scale problem with the dynamics of service demands. Numerical results under both real-world datasets and synthetic network topologies demonstrate that our proposed model and algorithm allow an NFV service provider (NSP) to place a virtual network function and steer a traffic flow efficiently in terms of energy cost for a delay-guarantee SFC. Importantly, the results provide an insight into the optimal and approximation solutions for an NSP to select a suitable traffic engineering approach with regard to network dynamics.

Knowledge about future optical network traffic can be beneficial for network operators in terms of decreasing an operational cost due to efficient resource management. Machine Learning (ML) algorithms can be employed for forecasting traffic with high accuracy. In this paper we describe a methodology for predicting traffic in a dynamic optical network with service function chains (SFC). We assume that SFC is based on the Network Function Virtualization (NFV) paradigm. Moreover, other type of traffic, i.e. regular traffic, can also occur in the network. As a proof of effectiveness of our methodology we present and discuss numerical results of experiments run on three benchmark networks. We examine six ML classifiers. Our research shows that it is possible to predict a future traffic in an optical network, where SFC can be distinguished. However, there is no one universal classifier that can be used for each network. Choice of an ML algorithm should be done based on a network traffic characteristics analysis.

Using service function chains, Internet Service Providers can customize the use of service functions that process the network flows belonging to their customers. Each network flow is injected into a service chain according to the flow features. Since most of the malicious applications try not to get the proper analysis by imitating some valid and famous applications, classification based on simple flow features may waste processing power by using inappropriate service chains for evasive flows. In this paper, we have explored an application-aware classification approach using machine learning methods. Using CatBoost as a machine learning method, a model is created and used for traffic classification. We have provided some statistical reports on how this approach is compared with simple flow feature-based approaches in malicious environments and how feature selection can impact classification correctness. Choosing the most suitable number of features at the right time can beat traditional approaches in classification quality and provide better results in the service function chaining environment.

Producción Científica, Residential networks play a critical role in assuring that services or applications such as tele-work, tele-education, medical care, entertainment, home automation, among others, have the required resources to obtain an optimal performance. Although current residential gateways try to meet the Quality of Service (QoS) demands, the traditional networking paradigm does not have the appropriate mechanisms to address the heterogeneous and dynamic nature of the services running at home. In this context, a feasible solution consists of leveraging the flexibility and adaptability of the Software Defined Networking (SDN) and Network Functions Virtualization (NFV) paradigms to provide a differentiated traffic treatment intended to improve the QoS support of residential networks. The proposal takes advantage of the Service Function Chaining (SFC) concept intrinsic to NFV as well as the capacity of an SDN-based residential gateway to differentiate the traffic of a certain application. Thus, an association between an SFC and the differentiated traffic is stablished to apply a specific treatment. Besides, a comprehensive architecture composed of the software defined residential network (SDRN), the software defined access network (SDOAN) and the NFV-compliant ISP's edge cloud infrastructure is envisioned. This architecture would allow dramatically improving the life cycle management of the residential network from a centralized point which follows a user-centric approach., Ministerio de Ciencia, Innovación y Universidades (grants TEC2015-67834-R, TEC2017-84423-C3-1-P, RED2018-102585-T and 0677_DISRUPTIVE_2_E)

This paper considers service provider network design with a view of meeting application availability. Our primary goal is to design a service provider network using disparate network components and low-availability Virtual Network Functions (VNFs) while achieving high-availability Service Function Chains (SFCs). To this end, we attempt to answer the questions as to how much redundancy, and what type of redundancy should be incorporated in the network. Initially, we model a state-of-the-art provider architecture that is spread across the access, metro-core and backbone networks with associated discrete components such as switches, routers, optical equipment and data-centers. Our network design model leads to computing the amount of over-the-top (OTT) services that can be provisioned over a given network while achieving a particular availability. To this end, we formulate a constrained optimization model whose objective is profit maximization subject to availability measures that OTT services demand. We provide constraints of robustness that facilitate traffic churn. Four heuristics are also proposed with objectives that consider the breadth of the three key impacting parameters: VNF licensing cost, server utilization and delay optimization and the case for dynamic traffic. A simulation model presents comparative data for efficiency, latency and server utilization as well as validates our optimization model. The results stress the importance of an efficient optimization model in planning the network, as well as planning VNF placement ahead in time.

Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this article, we illustrate the integration of MEC into a current mobile networks’ architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

Service Function Chaining (SFC) is an emerging paradigm aiming to provide flexible service deployment, lifecycle management, and scaling in a micro-service architecture. SFC is defined as a logically connected list of ordered Service Functions (SFs) that require high availability to maintain user experience. The SFC protection mechanism is one way to ensure high availability, and it is achieved by proactively deploying backup SFs and installing backup paths in the network. Recent studies focused on ensuring the availability of backup SFs, but overlooked SFC unavailability due to network failures. This paper extends our previous work to propose a Hybrid Protection mechanism for SFC (HP-SFC) that divides SFC into segments and combines the merits of local and global failure recovery approaches to define an installation policy for backup paths. A novel labeling technique labels SFs instead of SFC, and they are stacked as per the order of SFs in a particular SFC before being inserted into a packet header for traffic steering through segment routing. The emulation results showed that HP-SFC recovered SFC from failure within 20–25 ms depending on the topology and reduced backup paths’ flow entries by at least 8.9% and 64.5% at most. Moreover, the results confirmed that the segmentation approach made HP-SFC less susceptible to changes in network topology than other protection schemes.

International audience; Software Defined Networking (SDN) and Network Function Virtualization (NFV) are complementary and core components of modernized networks. In this paper, we consider the problem of reconfiguring Service Function Chains (SFC) with the goal of bringing the network from a sub-optimal to an optimal operational state. We propose optimization models based on the makebefore-break mechanism, in which a new path is set up before the old one is torn down. Our method takes into consideration the chaining requirements of the flows and scales well with the number of nodes in the network. We show that, with our approach, the network operational cost defined in terms of both bandwidth and installed network function costs can be reduced and a higher acceptance rate can be achieved, while not interrupting the flows.

Network function virtualization can significantly improve the flexibility and effectiveness of network appliances via a mapping process called service function chaining. However, the failure of any single virtualized network function causes the breakdown of the entire chain, which results in resource wastage, delays, and significant data loss. Redundancy can be used to protect network appliances; however, when failures occur, it may significantly degrade network efficiency. In addition, it is difficult to efficiently map the primary and backups to optimize the management cost and service reliability without violating the capacity, delay, and reliability constraints, which is referred to as the reliability‐aware service chaining mapping problem. In this paper, a mixed integer linear programming formulation is provided to address this problem along with a novel online algorithm that adopts the joint protection redundancy model and novel backup selection scheme. The results show that the proposed algorithm can significantly improve the request acceptance ratio and reduce the consumption of physical resources compared to existing backup algorithms.

openaire: EC/H2020/723172/EU//5GPagoda Dynamic Service Function Chaining (SFC) is a technique that facilitates the enforcement of advanced services and differentiated traffic forwarding policies. It dynamically steers the traffic through an ordered list of Service Functions (SFs). Enabling SFC capabilities in the context of a Software Defined Networking (SDN) architecture is promising, as it takes advantage of the SDN flexibility and automation abilities to structure service chains and improve the delivery time. However, the delivery time depends also on the traffic steering techniques used by an SFC solution. This paper provides a closer look at the current SDN architectures for SFC and provides an analysis of traffic steering techniques used by the current SDN-based SFC approaches. This study presents a comprehensive analysis of these approaches using efficiency criteria. It concludes that the studied solutions are not efficient enough to be deployed in real-life networks, principally due to scalability and flexibility limitations. Accordingly, the paper identifies relevant research challenges.

Network Function Virtualization (NFV) provides higher flexibility for network operators and reduces the complexity in network service deployment. Using NFV, Virtual Network Functions (VNF) can be located in various network nodes and chained together in a Service Function Chain (SFC) to provide a specific service. Consolidating multiple VNFs in a smaller number of locations would allow decreasing capital expenditures. However, excessive consolidation of VNFs might cause additional latency penalties due to processing-resource sharing, and this is undesirable, as SFCs are bounded by service-specific latency requirements. In this paper, we identify two different types of penalties (referred as "costs") related to the processingresource sharing among multiple VNFs: the context switching costs and the upscaling costs. Context switching costs arise when multiple CPU processes (e.g., supporting different VNFs) share the same CPU and thus repeated loading/saving of their context is required. Upscaling costs are incurred by VNFs requiring multi-core implementations, since they suffer a penalty due to the load-balancing needs among CPU cores. These costs affect how the chained VNFs are placed in the network to meet the performance requirement of the SFCs. We evaluate their impact while considering SFCs with different bandwidth and latency requirements in a scenario of VNF consolidation., Comment: Accepted for publication in IEEE Transactions on Cloud Computing

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms.

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-020-05372-x The transition towards full network virtualization will see services for smart ecosystems including smart metering, healthcare and transportation among others, being deployed as Service Function Chains (SFCs) comprised of an ordered set of virtual network functions. However, since such services are usually deployed in remote cloud networks, the SFCs may transcend multiple domains belonging to different Infrastructure Providers (InPs), possibly with differing policies regarding billing and Quality-of-service (QoS) guarantees. Therefore, efficiently allocating the exhaustible network resources to the different SFCs while meeting the stringent requirements of the services such as delay and QoS among others, remains a complex challenge, especially under limited information disclosure by the InPs. In this work, we formulate the SFC deployment problem across multiple domains focusing on delay constraints, and propose a framework for SFC orchestration which adheres to the privacy requirements of the InPs. Then, we propose a reinforcement learning (RL)-based algorithm for partitioning the SFC request across the different InPs while considering service reliability across the participating InPs. Such RL-based algorithms have the intelligence to infer undisclosed InP information from historical data obtained from past experiences. Simulation results, considering both online and offline scenarios, reveal that the proposed algorithm results in up to 10% improvement in terms of acceptance ratio and provisioning cost compared to the benchmark algorithms, with up to more than 90% saving in execution time for large networks. In addition, the paper proposes an enhancement to a state-of-the-art algorithm which results in up to 5% improvement in terms of provisioning cost. This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777067 (NECOS project) and the national project TEC2015-71329-C2-2-R (MINECO/FEDER). This work is also supported by the " Fundamental Research Funds for the Central Universities " of China University of Petroleum (East China) under Grant 18CX02139A

This paper investigates a multi-service activation procedure for telecommunications carriers’ zero-touch provisioning workflows using access network virtualization technologies in an optical access network. We examine a procedure in which one network function or a set of functions is initiated depending on the services to be activated after a line is automatically opened in response to a customer’s action. The feasibility of this procedure is experimentally verified in a joint testbed environment between three Asia-Pacific telecommunications carriers. The results showed that even when 32 customers simultaneously request service activation, it takes less than 30 seconds for all of the customers’ requests to be completed. In addition, we discuss potential implementations for activating various multi-service provisioning in response to customer requirements and network isolation in terms of virtualized resources and infrastructure. We also propose an implementation that utilizes network resources shared by various services more efficiently than that presented in our previous conference report, and we experimentally verify its feasibility. In addition, the experimental configurations briefly described in the previous report are described in detail in this paper.

International audience; The simple programming model and very low-overhead I/O capabilities of emerging packet processing techniques leveraging kernel-bypass I/O and poll-mode processing is gaining significant popularity for building high performance software middleboxes (aka Virtual Network Functions (VNFs)). However, existing OS schedulers fall short in rightsizing CPU allocation to poll-mode VNFs due to the schedulers' shortcoming in capturing the actual processing cost of these VNFs. This issue is further exacerbated by their inability to consider VNF processing order when VNFs are chained to form Service Function Chains (SFCs). The state-of-the-art VNF schedulers proposed as an alternative to OS schedulers are intrusive, requiring the VNFs to be built with scheduler specific libraries or having carefully selected scheduling checkpoints. This highly restricts the VNFs that can properly work with these schedulers. In this paper, we present UNi S, a User-space Non-intrusive work-flow aware VNF Scheduler. Unlike existing approaches, UNiS is non-intrusive, i.e., does not require VNF modifications and treats poll-mode VNFs as black boxes. UNiS is also workflow-aware, i.e., takes SFC processing order into account while scheduling VNFs. Testbed experiments show that UNi S is able to achieve a throughput within 90% and 98% of that achievable using an intrusive cooperative scheduler for synthetic and real data center traffic, respectively.

Network Function Virtualization (NFV) has revolutionized the way network services are offered, leading Enterprise and Service Providers to increasingly adapt their portfolio of network products in order to reap the benefits of flexible network service deployment and cost reduction promises. With this method, network services are offered in the form of software images instead of dedicated hardware. However, NFV presents several challenges, including standard networking challenges (e.g., security, resilience, and availability), management and orchestration challenges, resource allocation challenges, and performance trade-off challenges of using standard x86 servers instead of dedicated and proprietary hardware. The first three challenges are typical challenges found in virtualization environments and have been extensively addressed in the literature. However, the performance trade-off challenge can be the most impactful when offering networking services, negatively affecting the throughput and delay performance achieved. Thus, in this paper, we investigate and propose several configurations on a virtualized system for increasing the performance in terms of throughput and delay while chaining multiple virtual network functions (VNFs) in case of an undersubscribed and oversubscribed system, where the resource demands exceeds the physical resource capacity. Specifically, we use the Single Root Input Output Virtualization (SR-IOV) as our Input/Output (I/O) technology, and analyze the attainable throughput and delay when running multiple chained VNFs in a standard x86 server under various resource footprints and network features configurations. We show that the system throughput and delay in a multi-chained environment, offering multiple features, and under oversubscription can affect the overall performance of VNFs.

Network function virtualization has introduced a high degree of flexibility for orchestrating service functions. The provisioning of chains of service functions requires making decisions on both (1) placement of service functions and (2) scheduling of traffic through them. The placement problem (1) can be tackled during the planning phase, by exploiting coarse-grained traffic information, and has been studied extensively. However, runtime traffic scheduling (2) for optimizing system utilization and service quality, as required for future edge cloud and mobile carrier scenarios, has not been addressed so far.We fill this gap by presenting a queuing-based system model to characterize the runtime traffic scheduling problem for service function chaining. We propose a throughput-optimal scheduling policy, called integer allocation maximum pressure policy (IA-MPP). To ensure practicality in large distributed settings, we propose multi-site cooperative IA-MPP (STEAM), fulfilling runtime requirements while achieving near-optimal performance. We examine our policies in various settings representing real-world scenarios. STEAM closely matches IA-MPP in terms of throughput, and significantly outperforms (possible adaptations of) existing static or coarse-grained dynamic solutions, requiring 30%-60% less server capacity for similar service quality. Our STEAM prototype shows feasibility running on a standard server.

In recent years, cloud computing is evolving towards a distributed paradigm called Fog Computing, aiming to provide a distributed infrastructure by placing computational resources close to end-users. To fully leverage on Fog Computing, proper resource allocation is needed to cope with the demanding constraints introduced by IoT (e.g. low latency, high mobility). One of the main challenges that remain is Service Function Chaining (SFC). Services must be connected in a specific order forming an SFC allowing providers to benefit from the high flexibility and low operational costs introduced by network softwarization. In the demonstration, an SFC controller able to optimize the placement of service chains in Fog-cloud environments will be presented. The SFC controller has been implemented on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our approach allows Kubernetes to deploy micro-services based on up-to-date information on the current status of the network infrastructure. The demonstration will show how application developers could use our approach to set up service chains for their services. Then, performance outcomes of our SFC controller will be shown, especially in terms of container deployment times.

Fixed and mobile telecom operators, enterprise network operators and cloud providers strive to face the challenging demands coming from the evolution of IP networks (e.g. huge bandwidth requirements, integration of billions of devices and millions of services in the cloud). Proposed in the early 2010s, Segment Routing (SR) architecture helps face these challenging demands, and it is currently being adopted and deployed. SR architecture is based on the concept of source routing and has interesting scalability properties, as it dramatically reduces the amount of state information to be configured in the core nodes to support complex services. SR architecture was first implemented with the MPLS dataplane and then, quite recently, with the IPv6 dataplane (SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering of packets across nodes to a general network programming approach, making it very suitable for use cases such as Service Function Chaining and Network Function Virtualization. In this paper we present a tutorial and a comprehensive survey on SR technology, analyzing standardization efforts, patents, research activities and implementation results. We start with an introduction on the motivations for Segment Routing and an overview of its evolution and standardization. Then, we provide a tutorial on Segment Routing technology, with a focus on the novel SRv6 solution. We discuss the standardization efforts and the patents providing details on the most important documents and mentioning other ongoing activities. We then thoroughly analyze research activities according to a taxonomy. We have identified 8 main categories during our analysis of the current state of play: Monitoring, Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path Encoding, Network Programming, Performance Evaluation and Miscellaneous..., SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIALS

In order to provide interoperability of cross-domain IoT applications involving different IoT platforms, the authors previously proposed a virtual IoT system called VirIoT. The proposed system is composed of two functionalities: ThingVisor and vSilo, and it aims at decoupling IoT device providers and IoT application developers. ThingVisor enables to produce virtual IoT devices, or Virtual Things, from physical IoT devices for sharing the physical devices among cross-domain IoT applications. In addition, vSilo enables to bridge between such Virtual Things and IoT applications for the interoperability of cross-domain IoT devices. In this paper, in order to enhance the VirIoT system, we propose ThingVisor Factory that helps to design ThingVisors in a user-friendly way and deploy them on demand autonomously by following container orchestration methodologies, such as Kubernetes. ThingVisor Factory is based on two concepts: dataflow programming-based Graphical User Interface (GUI) and service function chaining-based ThingVisor development.

Most of today's cloud applications are delivered by Cloud Service Providers (CSPs) on top of a physical network managed by one or multiple Infrastructure Providers (InPs). This new way of delivering services is impacting InPs' revenues, as InPs are only responsible for transporting data to users. Network Function Virtualization (NFV) was proposed to help InPs gain more flexibility in provisioning new services over their networks, hence achieving lower capital and operational costs, keeping stable revenue margins, and resisting the competition of CSPs (e.g., the “Over-The-Top”players). NFV aims at moving from the traditional approach of network functions running over dedicated hardware (e.g., firewall, NAT, etc.) into virtualized software modules running on top of Commercial Off The Shelf (COTS) equipment. However, deploying NFV in an operational network requires addressing two fundamental problems. The first consists on determining the locations where Virtual Network Functions (VNFs) will be hosted (i.e., VNF placement) and the second on how to properly steer network traffic to traverse the required VNFs in the right order (i.e, routing), thus provisioning network services in the form of Service Function Chains (SFCs). In this work we try to solve both problems focusing our analysis on a metro-regional scenario, where link bandwidth and COTS node processing capacity is inherently limited and where the current trend consists on moving towards a Fixed and Mobile Convergence (FMC) network infrastructure. We propose and compare different heuristic strategies for SFC provisioning, characterized by latency and/or capacity awareness (i.e., able to best exploit latency of links and/or processing capacity of COTS nodes for an effective placement of VNFs) and by the adoption of a load balancing policy for traffic routing, with the aim of maximally consolidating VNFs. We assess the benefits of our strategies against a state-of-the-art algorithm, both in terms of number of required COTS nodes in the metro/access network and of SFC acceptance ratio. Our findings indicate that combining latency and capacity awareness in the VNF placement process with a load-balancing routing strategy brings high benefits in terms of VNF consolidation and SFC acceptance ratio.

Service function chaining (SFC) is able to provide customizable network function services to the traffic flows of different IoT subjects. Nowadays, SFC becomes profound to implement the service requirements of different IoT devices with the flexibility and programmability provided by emerging technologies, software defined network (SDN) and network function virtualization . These techniques play an increasingly important role for service deployment and allow the service requirement for certain IoT device to be specified by different subjects, including SDN applications and network managers. However, independent generation of SFC policies by multiple policy makers over the same device may introduce several problems in the process of deploying SFCs to IoT network. Turning the individual considerations into coherent global SFC policies can be challenging. It requires special process of composition and transition, considering the scenario of combining policies with different concerns specified by different entities who have no insight into the policies of others. In this paper, we propose a composition method to solve the anomalies existing in the process of composing distinct policies in the environment of IoT network with multiple IoT service managers. We design two algorithms for the proposed anomaly-free policy composition method, and implement a prototype. Extensive experiment results show that our proposed method can eliminate the anomalies between policies and only induces trivial overhead in the process of generating data plane rules.

Cyber-physical-social systems exploit the relationship between humans, computers, and the physical environment to provide high-quality real-time and personalized services. Provisioning services for applications in networks of large datacenters (DCs) is carried out as service function chaining (SFC) by linking ordered list of functions. With the development of software, defined networks and network function virtualization, SFC has gained more attention in the industry for deploying larger applications. Provisioning different service functions (SFs) of an SFC in different DC in a multi DC environment is a challenging task. Existing works on serving SFC select a service function path (SFP) along which the SFs are executed. However, none of the works provide the optimal solutions considering both the execution and data-transfer delay along with electric-optic-electric (EOE) conversion delay. In this paper, an optimal solution for SFP selection has been developed considering processing, transmission, and EOE conversion delay. The proposed dynamic solution can work in a heterogeneous environment. Performance evaluation of the proposed algorithm shows that, significant performance improvement in terms of total end-to-end execution delay of an SFC can be achieved compared with the state-of-the-art works.

5G network is envisioned to provide massive connectivity for a wide range of applications, such as ultra-clear media, internet of vehicles, and smart home. The traditional way of providing security services is difficult to support these new 5G applications flexibly and effectively. In our previous work, we proposed a SFC-based framework that chains security functions in different domains to provide security services on demand. However, creating cross-domain service function chains will inevitably result in the additional network latency. In this paper, we study this problem of minimizing the end-to-end latency when deploying cross-domain service function chains for 5G applications. First, an exact approach, consisting of service chain partition and service subchain embedding, is proposed to derive an optimal solution for cross-domain service function chain placement. Second, we improve the Viterbi algorithm and propose an efficient heuristic approach to derive near-optimal solutions for large networks. We also compare the performance of the proposed exact approach, the proposed heuristic approach, and the simple greedy approach in different scales of network infrastructures. Simulation results are presented to demonstrate the effectiveness of the proposed approaches.

Service Function Chaining (SFC) is the problem of deploying various network service instances over geographically distributed data centers and providing inter-connectivity among them. The goal is to enable the network traffic to flow smoothly through the underlying network, resulting in an optimal quality of experience to the end-users. Proper chaining of network functions leads to optimal utilization of distributed resources. This has been a de-facto model in the telecom industry with network functions deployed over underlying hardware. Though this model has served the telecom industry well so far, it has been adapted mostly to suit the static behavior of network services and service demands due to the deployment of the services directly over physical resources. This results in network ossification with larger delays to the end-users, especially with the data-centric model in which the computational resources are moving closer to end users. A novel networking paradigm, Network Function Virtualization (NFV), meets the user demands dynamically and reduces operational expenses (OpEx) and capital expenditures (CapEx), by implementing network functions in the software layer known as virtual network functions (VNFs). VNFs are then interconnected to form a complete end-to-end service, also known as service function chains (SFCs). In this work, we study the problem of deploying service function chains over network function virtualized architecture. Specifically, we study virtual network function placement problem for the optimal SFC formation across geographically distributed clouds. We set up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs). We consider link delays and computational delays in our model. The link queues are modeled as M/D/1 (single server/Poisson arrival/deterministic service times) and server queues as M/M/1 (single server/Poisson arrival/exponential service times) based on the statistical analysis. In addition, we present a novel affinity-based approach (ABA) to solve the problem for larger networks. We provide a performance comparison between the proposed heuristic and simple greedy approach (SGA) used in the state-of-the-art systems. Greedy approach has already been widely studied in the literature for the VM placement problem. Especially we compare our proposed heuristic with a greedy approach using first-fit decreasing (FFD) method. By observing the results, we conclude that the affinity-based approach for placing the service functions in the network produces better results compared against the simple greedy (FFD) approach in terms of both, total delays and total resource cost. We observe that with a little compromise (gap of less than 10% of the optimal) in the solution quality (total delays and cost), affinity-based heuristic can solve the larger problem more quickly than ILP. This work has been supported under the grant ID NPRP 6 - 901 - 2 - 370 for the project entitled "Middleware Architecture for Cloud Based Services Using Software Defined Networking (SDN)", which is funded by the Qatar National Research Fund (QNRF) and a grant from Huawei. H. Anthony Chan was supported by Huawei. The statements made herein are solely the responsibility of the authors. Scopus

The adoption of Network Function Virtualization and Software Defined Networking technologies allow network infrastructure operator flexibly orchestrating resources to provide tenants with their own virtual network. However, access to computing and network resource management APIs is typically allowed only within the infrastructure domain and rarely disclosed to tenants for security and performance reasons. This may severely limit tenants capability in coping with demands of application-tailored network services, including Service Function Chaining (SFC). While the literature extensively addressed the challenges of SFC in the infrastructure domain, tenant-side SFC management is quite unexplored yet, although discussed also by a standardization group. This work proposes an SFC platform (called SFCLola) providing tenants with a latency-aware SFC management while minimizing support required from infrastructure operators. The platform encompasses two main levels: an end-to-end chain management level featuring a VNF selection algorithm and a forwarding mechanism that can be programmed and enforced within the tenant network of VMs without requiring access to the switches at the network infrastructure data plane. SFCLola has been implemented as software prototype and experimentally evaluated on a multi-DC infrastructure provided by the 5GINFIRE project.

International audience; Network Functions Virtualization (NFV) enablesthe complete decoupling of network functions from proprietaryappliances and runs them as software applications on general–purpose servers. Service Function Chains (SFC) are paths with anordered sequence of network functions that have to be processed.In this paper, we consider the problem of reconfiguring SFCswith the goal of bringing the network from a sub-optimal toan optimal operational state. We propose optimization modelsbased on themake-before-breakmechanism, in which a new SFCis set up before the old one is torn down. Our method takes intoconsideration the chaining requirements of the flows and scaleswell with the number of nodes in the network. We show that,with our approach, the network operational cost defined in termsof both bandwidth and installed network function costs can bereduced and a higher acceptance rate can be achieved.

International audience; This paper deals with virtualised Network Function Placement and chaining over cloud and NFV environments. We propose a polynomial-time algorithm for joint VNF chain placement that is based on a recursive optimization approach. The key idea is to benefit from the robustness of the recursion paradigm that was used to solve many optimization problems. We demonstrate the potential of our solution by comparing it to previous work. We conduct performance evaluation first through extensive simulations and then using a real cloud testbed. The obtained results show that our proposed algorithm achieves better performance in terms of resource usage, execution time, acceptance rate and revenue of the cloud provider

International audience; Software-Defined Networking is a new approach to the design and management of networks. It decouples the software-based control plane from the hardware-based data plane while abstracting the underlying network infrastructure and moving the network intelligence to a centralized software-based controller where network services are deployed. The challenge is then to efficiently provision the service chain requests, while finding the best compromise between the bandwidth requirements, the number of locations for hosting Virtual Network Functions (VNFs), and the number of chain occurrences. We propose two ILP (Integer Linear Programming) models for routing service chain requests, one of them with a decomposition modeling. We conduct extensive numerical experiments, and show we can solve exactly the routing of service chain requests in a few minutes for networks with up to 50 nodes, and traffic requests between all pairs of nodes. We investigate the best compromise between the bandwidth requirements and the number of VNF nodes.

Lately application service providers (ASPs) and Internet service providers (ISPs) are being confronted with the unprecedented challenge of accommodating increasing service and traffic demands from their geographically distributed users. Many ASPs and ISPs, such as Facebook, Netflix, AT&T and others have adopted micro-service architecture to tackle this problem. Instead of building a single, monolithic application, the idea is to split the application into a set of smaller, interconnected services, called micro-services (or simply services). Such services are lightweight and perform distinct tasks independent of each other. Hence, they can be deployed quickly and independently as user demands vary. Nevertheless, scheduling of micro-services is a complex task and is currently under-researched. In this work, we address the problem of scheduling micro-services across multiple clouds, including micro-clouds. We consider different user-level SLAs, such as latency and cost, while scheduling such services. Our aim is to reduce overall turnaround time for the complete end-to-end service in service function chains and reduce the total traffic generated. In this work we present a novel fair weighted affinity-based scheduling heuristic to solve this problem. We also compare the results of proposed solution with standard biased greedy scheduling algorithms presented in the literature and observe significant improvements. This publication was made possible by the NPRP award [NPRP 6-901-2-370] from the Qatar National Research Fund (a member of The Qatar Foundation) and Huawei Technologies. The statements made herein are solely the responsibility of the author[s]. References [1] L. Sorensen and K. E. Skouby, "User Scenarios 2020 - a worldwide wireless future report," WWRF, July 2009. Scopus

International audience; This paper addresses the problem of Virtualized Network Functions placement and traffic steering in Cloud infrastructures. We design an efficient dynamic programming (DP) algorithm for joint VNF placement and traffic steering that runs in polynomial time. In compliance with dynamic programming approaches, we organize the problem in smaller interdependent subproblems (one per VNF in the requested chain) that are solved successively and aggregated to compose the overall chain placement. We demonstrate the potential of our solution by comparing it with a Multi-Stage approach and a greedy algorithm. Simulation results show that our dynamic programming algorithm achieves better performance in terms of resource usage, acceptance rate and cloud provider revenue

International audience; When deploying network service function chains the focus is usually given on metrics such as the cost, the latency, or the energy and it is assumed that the underlying cloud infrastructure provides resiliency mechanisms to handle with the disruptions occurring in the physical infrastructure. In this position paper, we advocate that while usual performance metrics are essential to decide on the deployment of network service function chains, the notion of resiliency should not be neglected as the choice of virtual-to-physical placement may dramatically improve the ability of the service chains to handle with failures of the infrastructure without requiring complex resiliency mechanisms.

The Network Function Virtualization paradigm enables the possibility to dynamically instantiate Virtual Network Functions (VNFs) in Commercial-Off-The-Shelf (COTS) hardware. Such VNFs are then concatenated together in Service Chains (SCs) to provide specific Internet services to the users. Depending on latency requirements for such services and considering the aim of maximally consolidating the VNFs (i.e., of minimizing the COTS hardware), the VNFs can be centralized in few datacenters in the core network or they can be distributed closer to the edge of the network. In this paper we evaluate the impact of latency requirements of SCs on VNF distribution towards the edge of the network, by also showing the benefits of a Fixed and Mobile Convergent (FMC) metro/access network, with respect to a non-convergent network, in terms of consolidation.

openaire: EC/H2020/723172/EU//5GPagoda Large-scale networking infrastructures such as service providers deploy complex services to deal with the growing network traffic demand, security concerns, and user preferences. Using Service Function Chaining (SFC) , a set of networking and management operations, permits to steer the traffic through a list of intermediate services. Traffic steering for SFC is usually based on packet headers to share the SFC information, however such headers introduce encapsulation overhead and require service functions support. In this paper, we present a novel traffic steering technique based on a compact SFC header. The proposed header does not increase the packet size and allows network operators to deploy SFC using legacy service functions. We also present a new SDN architecture for SFC based on compact headers. Our proposal permits a scalable and a flexible SFC deployment in real-life infrastructures.