Network Function Virtualization Research Articles

Backup Resource Allocation Model with Probabilistic Protection Considering Service Delay

This paper proposes a backup resource allocation model for virtual network functions (VNFs) to minimize the total allocated computing capacity for backup with considering the service delay. If failures occur to primary hosts, the VNFs in failed hosts are recovered by backup hosts whose allocation is pre-determined. We introduce probabilistic protection, where the probability that the protection by a backup host fails is limited within a given value; it allows backup resource sharing to reduce the total allocated computing capacity. The previous work does not consider the service delay constraint in the backup resource allocation problem. The proposed model considers that the probability that the service delay, which consists of networking delay between hosts and processing delay in each VNF, exceeds its threshold is constrained within a given value. We introduce a basic algorithm to solve our formulated delay-constraint optimization problem. In a problem with the size that cannot be solved within an acceptable computation time limit by the basic algorithm, we develop a simulated annealing algorithm incorporating Yen's algorithm to handle the delay constraint heuristically. We observe that both algorithms in the proposed model reduce the total allocated computing capacity by up to 56.3% compared to a baseline; the simulated annealing algorithm can get feasible solutions in problems where the basic algorithm cannot.

Availability Model for Data Center Networks With Dynamic Migration and Multiple Traffic Flows

The evaluation of application availability is a crucial step in ensuring functionality of the data center network (DCN). The application of data center network is no more a chain of physical devices, but a chain of virtualized network function (VNF) dynamically installed in servers, i.e., the service function chain (SFC), due to the adaptation of network function virtualization (NFV). When dynamic migration is considered, the application availability evaluation would encounter problems since the state of VNF components is not independent. And in the application deployed in DCN, there is multiple traffic flow because of the serialization of 1+1 (active-standby) and N-way (load-sharing) redundant VNF, however only single flow is considered in current availability model. When dynamic migration and multiple traffic flows are considered, the application availability evaluation would encounter the following problems: 1) The application behavior is not described in the existing availability model; 2) The availability model could not quantify the influence of different traffic of each flow. This paper proposes an availability model, which consist of network evolution model (NEM) of DCN and the availability evaluation algorithm based on NEM (AEA-NEM), to evaluate the availability of different applications deployed on DCN. The NEM of DCN describes the dynamical process of migration and switch of three kinds of VNF (normal, 1+1 and N-way) in application, and the AEA-NEM evaluate the availability of application with multiple traffic flows. The simulation results show that, the steady state availability of the proposed model is consistent with it of generalized stochastic Petri net (GSPN) model. And the model could evaluate the application availability with dynamic migration and multiple traffic flows in DCN. Based on the current VNF placement and redundant strategy, the most available designing scheme is analyzed.

Service Deployment Model with Virtual Network Function Resizing Based on Per-Flow Priority

This paper investigates a service deployment model for network function virtualization which handles per-flow priority to minimize the deployment cost. Service providers need to implement network services each of which consists of one or more virtual network functions (VNFs) with satisfying requirements of service delays. In our previous work, we studied the service deployment model with per-host priority; flows belonging to the same service, for the same VNF, and handled on the same host have the same priority. We formulated the model as an optimization problem, and developed a heuristic algorithm named Flex Size to solve it in practical time. In this paper, we address per-flow priority, in which flows of the same service, VNF, and host have different priorities. In addition, we expand FlexSize to handle per-flow priority. We evaluate per-flow and per-host priorities, and the numerical results show that per-flow priority reduces deployment cost compared with per-host priority.

Look-Ahead VNF-FG Embedding Framework for Latency-Sensitive Network Services

Dynamic and zero-touch management is expected to be the key feature of next-generation 6G networks. Network Function Virtualization (NFV) is one of the key technologies for realizing such management through software-based networks. Despite great benefits offered by NFV, deploying network services (NSs) in NFV ecosystems remains a challenge, especially for latency-sensitive NSs, as they demand stringent latency requirements and fast service provisioning. Specifically, service graphs should be embedded into an infrastructure such that these requirements are satisfied while optimizing network operator’s objectives. To cope with the scalability of optimization-based approaches, heuristic methods are known as promising alternatives to find a satisfactory solution within an acceptable execution time. However, existing VNF embedding heuristics still suffer from the so-called causality issue, which may degrade the embedding solution quality. The causality issue means that embedding decisions cannot be optimally determined before all neighboring dependencies are known. To this end, we introduce our h-horizon sequential look-ahead greedy embedding framework, which provides efficient embedding and re-embedding strategies to alleviate the impact of the causality issue. The simulation results indicate that our proposed algorithm significantly improves embedding cost, compared to the existing heuristic algorithms while being much more scalable than an optimization-based approach.

Runtime Adaptation Framework for Fulfilling Availability and Continuity Requirements of Network Services

The Network Function Virtualization (NFV) framework is an enabler for the automation of Network Service (NS) management. In the context of NFV, an NS is realized by interconnecting Virtual Network Functions (VNF) using Virtual Links (VL). Availability and continuity are among the important characteristics of an NS. These characteristics depend on the availability of the VNFs and VLs composing the NS, which are usually selected at NS design time. VNFs and VLs utilize the resources of the underlying infrastructure, and their availability (partially) depends on the availability of these resources. To design an NS to fulfill availability and continuity requirements, the availability required from the resources is constrained at design time. However, the characteristics of these resources may change at runtime due to the dynamicity of NFV infrastructure. Thus, impacting the availability of the VNFs and the VLs, which in turn may impact the availability and continuity of the NS. To fulfill these requirements at runtime despite the changes in the infrastructure, the NS should be adapted. In this paper, we propose a framework for the runtime adaptation of NSs that reacts to changes and adapts the NS configuration so that it can fulfill its availability and continuity requirements during the NS lifetime. We also propose a method to develop machine learning models that are used within the framework to determine the required adjustments at runtime. We implemented the proposed framework, the method for developing the machine learning models, a testbed, and NSs to assess the feasibility and validity of our approach through experiments.

Dynamic Service Chaining for Ultra-Reliable Services in Softwarized Networks

Network softwarization is a paradigm shift for the next generation of network. Network Function Virtualization (NFV) softwarizes network functions as virtual network function (VNF) instances on top of a network infrastructure. Along with the merits of flexibility, programmability and reduced function provisioning cost, softwarization of network functions introduces new challenges of service’s reliability due to possible hardware failures, software bugs and hacker attacks. Currently reliable service provisioning schemes based on first-order statistics fails in accounting for the ultra-reliable needs of mission-critical services. In this paper, we propose a dynamic service chaining (DSC) framework to provision ultra-reliable services where the reliability is characterized by the probability distribution using extreme value theory. Our design objective is to minimize the number of backup VNF modules subject to reliability and resource constraints. Due to the dynamic nature of network, primary and backup VNFs are re-mapped to higher reliable physical machines in order to provide ultra-reliable services. Using Lyapunov stochastic optimization, primary VNF mapping and backup VNF selection are performed in the large and small timescales respectively. Numerical results show that the proposed DSC framework can guarantee ultra-reliable network services efficiently.

Intelligent and Resource-Conserving Service Function Chain (SFC) Embedding

Network Function Virtualization (NFV) opens us great opportunities for network processing with higher resource efficiency and flexibility. In this respect, there is an increasing need for intelligent orchestration mechanisms, such that NFV can exploit its potential and live up to its promise. Genetic algorithms have emerged as a promising alternative to the proliferation of heuristic and exact methods for the Service Function Chain (SFC) embedding problem. To this end, we design and evaluate a genetic algorithm (GA), which computes efficient embeddings with runtimes on par with approximate methods. We present a GA model as state-space search in order to clarify the design choices of a GA. Our proposed GA utilizes a heuristic for the generation of the initial population, with the aim of directing the search towards the solution. Given the sensitivity of GAs on their various parameters, we introduce a parameter adjustment framework for GA fine-tuning. A comparative evaluation among a range of GA variants with diverse features sheds light on the impact of these features on SFC embedding efficiency. The GA variant that stands out is further benchmarked against a baseline greedy algorithm and a state-of-the-art heuristic. Our evaluation results indicate that the GA yields notable gains in terms of request acceptance and resource efficiency.

Secure and Intelligent Service Function Chain for Sustainable Services in Healthcare Cyber Physical Systems

The gradual integration of Internet of Things (IoT) devices has made the healthcare cyber physical systems (CPSs) ubiquitous and complex. Since IoT devices are heterogeneous, it is crucial to virtualize these devices in a general way so as to orchestrate and allocate their equipped functions and resources in a general manner. Network function virtualization (NFV) is the key networking technology towards the device virtualization. Within NFV, service function chain (SFC) is the crucial issue. While in real networking environment, physical device accidentally fails. How to guarantee the SFC, deployed on top of one or more failed devices, recover to work has not been well addressed yet. Hence, we research this issue, aiming at realizing the secure and sustainable SFC placement and scheduling in an intelligent manner. An intelligent SFC placement and scheduling framework, labeled as <i>Sec-SFC-Intell</i>, is proposed in this paper. When receiving a SFC request, the <i>Sec-SFC-Intell</i> will deploy the SFC on top of the healthcare CPSs. When the failure of certain one physical device is detected, the affected SFC will be re-placed and re-scheduled in order to recover to run and serve. In order to prove the feasibility and highlight the merits of <i>Sec-SFC-Intell</i>, simulation work is conducted. Compared with two compared benchmarks, <i>Sec-SFC-Intell</i> has an apparent SFC acceptance advantage of nearly <inline-formula><tex-math notation="LaTeX">$10\%$</tex-math></inline-formula>.

Slice-Based Service Function Chain Embedding for End-to-End Network Slice Deployment

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.

An Approach to Network Service Placement Reconciling Optimality and Scalability

The inevitable transition from physical dedicated hardware devices towards lightweight containerized reusable software modules with Network Function Virtualization (NFV) introduces countless opportunities while presenting several unprecedented challenges. Satisfying NFV expectations in post-5G networks heavily depends on the efficient placement of network services. In this paper, after modeling the placement problem and proposing the exact resolutions using ILP and CG, we propose our deterministic placement solution, capable of obtaining optimal results with the scalability of a heuristic-grade approach. Our method is organized as a BnB structure, applying AI search strategies (especially A*) to address the problem of network service placement. We believe that it is suitable for a range of applications in online placement scenarios, whether we concentrate on the quality of the results or on the strict time constraints. We are interested in the popular objective of SA maximization and have carried out several extensive evaluations. The obtained results confirm the effectiveness of our solution.

Performance Evaluation of a Novel Intrusion Detection System in Next Generation Networks

The integration of Internet of Things (IoT) with 5G simply creates additional threat landscape and any network infrastructure is more vulnerable. Severe attacks on networks potentially damage organization reputation, customers or tenants lose confidence, and impacts operational and maintenance cost. Intrusion detection systems (IDSs) are an effective approach to mitigate threats. We present a novel IDS mechanism in which the unique Radio Frequency (RF) features of IoT devices are used to create a learning model which is later used to identify the illegitimate devices in the network. Leveraging the Deep Autoencoder (DAE), the existing steady-state feature extraction is generalized. The performance evaluation is conducted using a real data set from different aspects including the mobility of the nodes. The proposed IDS is broken down into pluggable virtual network function (VNF) components and its evaluation is presented for its integration into the 5G network slicing ecosystem from the perspective of the European Telecommunications Standards Institute (ETSI) standards. A Proof of Concept (PoC) is presented using ETSI Open Source NFV Management and Orchestration (OSM-MANO) test bed, deployed on AWS cloud systems, to show how the proposed approach would fit in with a real-life MANO.

RuleDRL: Reliability-Aware SFC Provisioning With Bounded Approximations in Dynamic Environments

As a key enabling technology for 5G, network function virtualization abstracts services into software-based service function chains (SFCs), facilitating mission-critical services with high-reliability requirements. However, it is challenging to cost-effectively provide reliable SFCs in dynamic environments due to delayed rewards caused by future SFC requests, limited infrastructure resources, and heterogeneity in hardware and software reliability. Although deep reinforcement learning (DRL) can effectively capture delayed rewards in dynamic environments, its trial-and-error exploration in a vast solution space with massive infeasible solutions may lead to frequent constraint violations and traps in poor local optima. To address these challenges, we propose a RuleDRL algorithm that combines the capability of DRL to capture delayed rewards and the strength of rule-based schemes to explore high-quality solutions without violating constraints. Specifically, we first formulate the reliable SFC provision problem as an integer nonlinear programming problem, which is proven to be NP-hard. Then, we jointly design DRL and rule-based schemes that are coupled to make the final decision and establish a bounded approximation ratio in general cases. Extensive trace-driven simulations show that RuleDRL can save the total cost by up to 65.67% and improve the SFC acceptance ratio by up to 82%, compared to the state-of-the-art solution.

Low-Latency and Reliable Virtual Network Function Placement in Edge Clouds

Low-Latency and Reliable Virtual Network Function Placement in Edge Clouds

Salience-based VNF placement method for evolving multiple network slices

Network Function Virtualization (NFV) technology enables the deployment of a distinct network slice composed of various virtualized network functions (VNFs) required for offering a communication service. As the user population or network traffic volume fluctuates, the capacity of the network slices must be dynamically adjusted. It is necessary to decide whether to place VNFs in appropriate locations such that the quality of services (QoS) is guaranteed while optimally utilizing the allocated resources. The above challenge can be addressed by solving an optimization problem, such as integer linear programming. However, as solving the optimization problem requires a considerable amount of time, it is not applicable in agile network control mechanisms. In this paper, we propose a network function placement algorithm based on salience, which is one of the characteristic metrics of links in a graph. Our proposal is aimed to provide the adequate placement in short time to achieve the following objectives of keeping the slice reconstruction cost low and avoiding the cases of QoS degradation. The simulation results show that our scheme performs equally well for different sized slices (i.e., the user population). We found that the network reconfiguration costs are low and almost the same for various sized slices when they are reconstructed by the proposed method in a substrate network of 200 nodes. Furthermore, we confirmed that the proposed method keeps the slice reconstruction cost low and avoids blocking of slice reconstruction even in the situations when a resource competition occurs among multiple slices.

Deployment of VNF service chains with grooming and resilience in elastic optical networks

Network function virtualization (NFV) uses virtual network functions (VNFs) and VNF service chains (VNF-SCs) as a flexible and cost-efficient way of providing network services. This new backbone plays a vital role in dealing with the outbreak of traffic patterns generated by diverse services. Elastic optical networks (EONs) can be used for maximum flexibility to interconnect the computing infrastructure and data centers (DCs) required to host these VNFs and effectively transport the heterogeneous traffic flowing through the VNF-SC. The aim of the paper is to provide a complete mixed integer linear programming (MILP) formulation for the problem of VNF-SC deployment with survivability to minimize the total number of functions, balance the load utilization through grooming, and avoid spectrum fragmentation in EONs. In addition, we propose an efficient heuristic solution in dealing with the problem of large networks. The performance of the proposed strategies is evaluated based on the availability of resources (function computing capability). Simulation results show that the proposed MILP and heuristic can reduce the total amount of allocated VNFs, thereby, allowing for a certain level of resilience.

NEST: Optimal deploying DAG-SFCs to maximize the flows wholly served in the network edge

Compared with the dedicated hardware-based middle-boxes, Network Function Virtualization (NFV) makes network services more flexible, scalable, and cost-efficient by replacing the network functions, e.g., Firewalls, Deep Packet Inspections (DPI), Load Balancers, by Virtual Network Functions (VNFs) running on general-purpose commercial servers. Multiple VNFs usually chain up to formulate Service Function Chains (SFC) to enable complex network services. With the exploitation of VNF parallelism, SFC with parallel VNFs (Para-SFC) is proposed to reduce the SFC execution delay. However, the inner-structure of a Para-SFC is usually complex and a Para-SFC may have multiple legitimate topologies. DAG-SFC is one of the basic sharping methods for standardizing the topology of Para-SFCs. To adapt to the fast development of edge computing, it is critical to find an optimal deployment of Para-SFCs in hybrid edge-and-cloud network with the purpose of maximizing the amount of the flows completely serviced in the edge. In this paper, we first analyze the target problem via integer program modeling and prove its NP-hardness. Then, we propose a heuristic algorithm named NEST, which is based on the combination of maximum spanning tree and Next Fit. With extensive evaluations, we demonstrate that, compared with the benchmark methods, NEST can achieve up to respective 14% and 37% performance gain on DAG-SFC edge deployment maximization while can always realize (up to respective 276% and 305%) higher efficiency of link usage.

Enabling realistic experimentation of disaggregated RAN: Design, implementation, and validation of a modular multi-split eNodeB

Software-defined networking and network function virtualization are the key enablers in the division of base station functionalities and the deployment of some of these in central nodes. In this way, eNodeB tasks, traditionally deployed at dedicated nodes, are distributed between Central Units (CU) and Distributed Units (DU), which are closer to the Radio Units (RU). The 3GPP has specified eight possible functional splits between the CU and the DU. Despite their importance, to date, there is a lack of practical approaches that enable the real separation of multiple functional splits into different nodes, which would in turn enable the adaptation of the functional split configuration, according to network conditions and traffic load. This paper presents the design and implementation of an experimental architecture that allows the actual separation of protocol layers into different nodes, enabling the experimentation with up to four functional split configurations. The validation results demonstrate the feasibility of the proposed implementation, which copes with the delay requirements established by the Small Cell Forum in all configurations.

Virtual Network Function Development for NG-PON Access Network Architecture

Modern networks urge agility, flexibility, and capacity to cope with the growing demand for media content and applications increasingly oriented toward data consumption. The Central Offices (CO) of telecommunication providers, being a vital aggregator of different access networks, such as optical and mobile, need to be prepared to deal with these demands. The Open Broadband-Broadband Access ion (OB-BAA) architecture fits into the initiative to modernize the Information Technology (IT) components of broadband networks, more specifically the COs. This paper discusses the development of a Virtualized Network Function (VNF) in the context of network security to be integrated as a component of an OB-BAA architecture guided by the Software-Defined Network paradigm. More specifically, the authentication and authorization of network equipment within the IEEE 802.1X protocol are applied to Next Generation Passive Optical Networks. The VNF development is based on the Golang language combined with gRPC programmable interfaces for communication between the various elements of the OB-BAA architecture, and then the components were “containerized” and inserted in the Docker and Kubernetes virtualization frameworks of a multinational telecommunications operator. Finally, performance metrics such as computational resource usage (CPU, memory, and network I/O) and execution time of VNF processes were analyzed in usage tests with multiple supplicants and distinct operational modes, to attest to the most promising virtualization scenarios.

Moving Target Defense for the cloud/edge Telco environments

The Internet of Things (IoT) paradigm has been one of the main contributors, in recent years, to the growth in the number of connected equipment. This fact has predominantly contributed to IoT being constrained by the 5th Generation Mobile Network (5G) progress and the promises this technology brings. However, this can be a double-edged sword. On the one hand, it will benefit from those progresses, but on the other, it will also be impacted by any security risk associated with 5G. One of the more serious security problems associated with it is the new wave of virtualization and softwarization of networks and analogous appliances, brought to light by paradigms such as Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC). Considering these predicaments, we propose a state-of-the-art Moving Target Defense (MTD) approach that defends Cloud-based Network Functions (CNFs) launched within MEC and NFV environments. Furthermore, our mechanism follows the famous Everything as a Service (XaaS) ideology, allowing any CNF provider to use this protection system, working agonistically. In the end, we created a Proof of Concept (PoC) of our proposed methodology, which we then used to conduct an extensive practical security analysis against the multiple phases of the Intrusion Kill Chain. Our final results have proven that our MTD as a Service (MTDaaS) approach can effectively delay and, in some cases, stop an attacker from achieving its objectives when trying to attack a CNF, even if the related vulnerability is a zero-day.