Virtual Network Function Instances Research Articles

Multi-objective optimization of SFC deployment using service aggregation and computing offload

New technologies such as virtualization and Software Defined Networking (SDN) have given traditional networks unprecedented flexibility. A Service Function Chain (SFC) formed by concatenating multiple Virtual Network Functions (VNFs) expands network functions to meet the growing personalized network requirements of applications. However, due to the decentralization of VNF instances and the tight cloud load, how to ensure Quality of Service (Qos) while deploying the SFC brings new challenges. Hence, a SFC deployment strategy based on multi-objective optimization, named MO-SACO is proposed, which regards delay optimization, reliability assurance and cost reduction as the main objectives. Specifically, we first design a VNF aggregation rule to handle raw SFC requests, which effectively reduces the SFC path latency and improves reliability. Then, VNF placement and traffic routing is modeled as a computation offloading decision based on cloud-fog-edge collaboration with the goal of target optimization according to functional, location, and resource constraints. Finally, we also take into account the performance changes of deployed SFCs and handle non-compliant requests in a timely manner. We conduct extensive simulations in networks of varying sizes and the results demonstrate that the proposed MO-SACO strategy not only achieves lower latency and cost, higher reliability compared with the state-of-the-art methods, but also has unexpected performance in terms of the deployment success rate and node load.

Bi-LSTM/GRU-based anomaly diagnosis for virtual network function instance

In edge–cloud networks, Network Function Virtualization (NFV) technology provides users with high-performance Virtual Network Functions (VNFs) to replace dedicated hardware devices. In this paper, we focus on the VNF instance (VNFI) anomaly diagnosis (AD). In large-scale production data centers, VNFI anomalies are inevitable, causing degradation in network service performance, while accurate VNFI AD can minimize the losses. The AD task can be abstracted as a multi-classification problem, where the VNFI state is classified as normal or a specific fault type. Existing studies have not proposed mature VNFI AD systems and feature selection criteria, and the accuracy of AD tasks can still be improved. To address these gaps, we first design the Distributed Online Anomaly Diagnosis and Broadcasting (DOADB) system, providing online VNFI AD daemons, offline parameter training, and anomaly broadcasting. Then, we establish VNFI feature selection criteria and construct a compelling VNFI feature dataset containing simulated and real-time data for each VNFI. Based on the DOADB system and feature dataset, we adopt the Bi-LSTM and Bi-GRU networks to realize the AD multi-classification algorithm, leveraging the bidirectional network structure and long-term memory to enhance AD accuracy. Experimental results demonstrate that the proposed system and algorithm can provide effective AD daemons for VNFIs and outperform existing algorithms in both accuracy and macro F1-score metrics.

Cost-efficient flow migration for SFC dynamical scheduling in geo-distributed clouds

Combining Network Function Virtualization (NFV) and Software Defined Network (SDN) enables the delivery of network services in a flexible and cost-efficient manner. NFV allows elasticity scheduling of Virtual Network Function (VNF) instances to adopt the dynamic variation of network flows, such as VNF instance scale out or in and load balancing. To implement the elasticity scheduling, existing flows need to be re-routed through the VNF instances. One major problem is how to select appropriate flows for migration to reduce migration cost and improve network performance after migration. In this paper, we propose a Cost-efficient Flow Migration framework, CFM, to jointly optimize flow migration cost and network performance after migration (including flow routing cost and maximum VNF instance load) for flow migration in Service Function Chain manner. Experimental results demonstrate that CFM can reduce flow routing cost after migration by 43%∼62% compared with previous works, while achieving similar flow migration cost in scale out/in scenario. In load balancing scenario, CFM can also reduce flow migration cost by 65%∼79% and flow routing cost after migration by 43%∼79% compared with previous works, while ensuring that the maximum VNF instance load does not significantly increase.

Digital Twin-Assisted, SFC-Enabled Service Provisioning in Mobile Edge Computing

Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We thirdly provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform their comparison benchmarks, and improve the performance of their comparison counterparts by no less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10.2 \%$</tex-math></inline-formula> .

Efficient Deployment and Scheduling of Shared VNF Instances in Mobile Edge Computing Networks

Efficient Deployment and Scheduling of Shared VNF Instances in Mobile Edge Computing Networks

Efficient Deployment and Scheduling of Shared VNF Instances in Mobile Edge Computing Networks

Efficient Deployment and Scheduling of Shared VNF Instances in Mobile Edge Computing Networks

Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing

The use of virtual network functions (VNFs) enables the implementation of service function chains (SFCs), which is an innovative approach for delivering network services. The deployment of service chains on the actual network infrastructure and the establishment of virtual connections between VNF instances are crucial factors that significantly impact the quality of network services provided. Current research on the allocation of vital VNFs and resource constraints on the edge network has overlooked the potential benefits of employing SFCs with instance reuse. This strategy offers significant improvements in resource utilization and reduced startup time. The proposed approach demonstrates superior performance compared to existing state-of-the-art methods in maintaining inbound service chain requests, even in complex network typologies observed in real-world scenarios. We propose a novel technique called African vulture optimization algorithm for virtual network functions (AVOAVNF), which optimizes the sequential arrangement of SFCs. Extensive simulations on edge networks evaluate the AVOAVNF methodology, considering metrics such as latency, energy consumption, throughput, resource cost, and execution time. The results indicate that the proposed method outperforms BGWO, DDRL, BIP, and MILP techniques, reducing energy consumption by 8.35%, 12.23%, 29.54%, and 52.29%, respectively.

HyScaler: A Dynamic, Hybrid VNF Scaling System for Building Elastic Service Function Chains Across Multiple Servers

Dynamically scaling Virtual Network Function (VNF) is essential for network operators to make their networks accommodate time-varying network traffic. However, current research only applies homogeneous VNF scaling or focuses on mathematical optimizations without implementations in a real-world environment. To build this gap, this paper presents HyScaler, a dynamic, hybrid VNF scaling system implemented in an open-source NFV platform. With HyScaler, we can build elastic Service Function Chains (SFCs) by scaling overloaded VNF Instances (VNFIs) across multiple CPU cores or physical servers. HyScaler implements this by (1) monitoring traffic loads for each deployed VNFI; (2) scaling overloaded VNFIs through all kinds of VNF scaling methods; and (3) placing and chaining the new VNFI using the Global VNF Placement and Chaining (GVPC) algorithm. Extensive experiments are conducted to validate the scalability of our HyScaler and the effectiveness of the GVPC algorithm. Compared with the original NFV platform, experimental results show that HyScaler can improve the performance of VNFIs by about 1.02 times.

SAFE: Service Availability via Failure Elimination Through VNF Scaling

Virtualized network functions (VNFs) enable software applications to replace traditional middleboxes, which are more flexible and scalable in the network service provision. This paper focuses on ensuring Service Availability via Failure Elimination (SAFE) using VNF scaling, that is, given the resource requirements of VNF instances, finding an optimal and robust instance consolidation strategy, which can recover from one instance failure quickly. To address the above problem, we present a framework based on rounding and dynamic programming. First, we discretize the range of resource requirements into several sub-ranges, and thus the number of instance types becomes a constant. Second, we further reduce the number of instance types by gathering several small instances into a bigger one. Third, we propose an algorithm built on dynamic programming to solve the instance consolidation problem with a limited number of instance types. Finally, we set up a testbed to profile the functional relationship between the resource and the throughput for different types of VNFs, and conduct simulations to validate our theoretical results according to profiling results. The simulation results show that our algorithm outperforms the standby deployment model by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$27.33\%$</tex-math> </inline-formula> on average in terms of the number of servers required. Furthermore, SAFE has marginal overheads, around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.22\%$</tex-math> </inline-formula> , compared to the instance consolidation strategy without VNF backup consideration.

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward.

Network Function Virtualization in Elastic Optical Networks

In Network Function Virtualization (NFV), traditional purpose-built proprietary middle-boxes are replaced with virtual network function (VNF) instances that are deployed over generic hardware; network traffic flows are steered through a sequence of VNF instances (i.e., that forms a service function chain) after resolving the VNF Forward Graph Embedding (FGE) problem. NFV eases the network management and fosters the agile deployment of network services, thus enabling a foremost solution to address issues of traditional enterprise networks. With the everlasting Internet traffic growth, in this work, we extend the provision of NFV upon Elastic Optical Networks (EONs) that deliver abundant elastic and fine-granular bandwidth for connections between VNFs. We systematically examine the integrated NFV-EON architecture, and investigate one key enabler, namely the FGE-EON problem, where one has to coordinate the allocation of computing resources (as in a typical FGE problem) and the assignment of optical bandwidth (i.e., spectrum allocation in EONs). The contributions of this work are multi-fold. The FGE-EON problem is proven to be NP-Complete for the first time. An Integer Linear Programming (ILP) model is developed to address FGE-EON problem of small instances. Despite its complexity, with novel modeling techniques, the proposed ILP model maintains a compact structure even after accounting for VNF sharing and VNF co-location together for the first time. For large scale problems, we design a heuristic algorithm based on layered graph that is shown to be near-optimal in our evaluations.

Virtual Network Function Migration Considering Load Balance and SFC Delay in 6G Mobile Edge Computing Networks

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.

Dynamic SFC placement scheme with parallelized SFCs and reuse of initialized VNFs: An A3C-based DRL approach

Mobile Edge Computing (MEC) is a well-known network architecture that extends cloud computing to the network edge. Compared with cloud computing, Network Function Virtualization (NFV) can provide flexible services in MEC for mobile users. Virtual Network Functions (VNFs) have emerged as software-based hardware middleboxes by NFV technology to host real-time applications. Basically, the combination of multiple VNF instances is defined as a Service Function Chain (SFC), which can provide dynamic service requirements in the MEC. Despite the rapid growth of MEC and the widespread support of service providers for SFC, many issues are still challenging and need to be addressed. In MEC scenarios with limited resources, the effective placement of SFCs with the aim of resource efficiency remains a challenging problem. Motivated by the scalability shortcomings of existing schemes to solve dynamic placement of SFCs, we propose Deep Reinforcement Learning (DRL)-based approaches to solve this problem, i.e., Asynchronous Advantage Actor-Critic (A3C). The proposed scheme is based on the reuse of initialized VNFs to improve the Quality of Service (QoS), which is developed with the aim of maximizing the long-term cumulative reward. In addition, a parallel processing approach of SFCs is included in the proposed scheme, which can split the traffic in each flow into sub-flows. This shares the processing load by instantiating duplicate instances of each VNF type in the SFC. The simulation results guarantee the efficiency of the proposed scheme and improves the average performance between 6% and 24% compared to the state-of-the-art clustering methods.

Truthful VNFI Procurement Mechanisms With Flexible Resource Provisioning in NFV Markets

With the rapid development of network function virtualization (NFV), more and more enterprises and operators are seeking network service provisioning via service chains of virtual network functions (VNFs), instead of depending on proprietary hardware appliances. Following this trend, an NFV market is emerging, where users can procure different VNF instances (VNFIs) and their combinations from multiple network service providers (NSPs) in a pay-as-you-go way. In such a procurement process, how to guarantee truthfulness while enabling flexible resource provisioning in form of VNFIs is a significant challenge. In this paper, we propose a truthful reverse combinatorial auction-based mechanism to solve the combinatorial VNFI procurement problem. To support flexible resource provisioning, this mechanism allows NSPs to be multi-minded, and determine the provisioning VNFIs according to the auction results. Specifically, we design a heuristic algorithm to determine the winning bids in polynomial time. Furthermore, we devise a critical-payment-based pricing algorithm to induce NSPs to disclose their real costs, aiming to achieve truthfulness. Rigorous theoretical analysis shows the proposed mechanism can guarantee truthfulness, individual rationality and computational efficiency. Simulation results also verify the effectiveness and efficiency of the proposed mechanism.

Network slicing in virtualized 5G Core with VNF sharing

Through multiplexing separate virtual networks on the same network infrastructure, network slicing will lead to customization, scalability, flexibility, and isolation of services in different domains of the network. It also results in lower CAPEX/OPEX via increased utilization of network resources. In this paper, we have formulated an Integer Linear Programming (ILP) model to create network slices in the 5G virtualized core by considering the sharing of virtual network functions (VNFs) between different network slices to minimize cost. The ILP model aims to minimize the number of VNF instances and resource consumption while meeting the network slice requirements. The main motivation for sharing the 5G core VNFs among different network slices is to reduce the number of VNF instances, which results in a lower management overhead of VNFs. As solving the optimization problems is time-consuming and does not scale for larger deployments, we also developed a Heuristic Backtracking Algorithm for Network Slicing (HBA-NS). Furthermore, in order to account for the dynamic nature of traffic, our proposed network slicing framework also includes a CNN-LSTM deep learning model to predict UEs’ requests in each network slice. Based on the predicted requests, the HBA-NS algorithm scales allocated resources of different network slices to meet the dynamic SLA requirements of users. Our evaluation results show that the solution achieved by the HBA-NS algorithm is on average 6% worse than the optimal solution in the worst case but derived at a significantly lower run time. We also evaluated the proposed network slicing framework in terms of different criteria including its isolation property. Our simulation results show that network slicing can provide better performance compared to competitive approaches.

Migration of VNF instances for service continuity

Fog computing computes and stores data locally based upon distributed architectures that extend cloud to the edge of the network. This enables fog paradigms to be suitable for delay‐sensitive applications that demand various network functions (NFs). These NFs here are provided using network function virtualization (NFV) to allow elastic NF deployment on the fog nodes, that is, virtual network functions (VNFs). Now a major challenge for NFV‐based fog architectures is the limited resources, which vulnerate them to premature saturation and load imbalance. Hence resource management schemes are necessary for fog networks, in particular load migration and redistribution. Along this, an efficient migration scheme is proposed here using the coordinated generalized pattern search (CGPS) heuristic method. The scheme migrates excess load from heavily‐loaded (HL) nodes to lightly‐loaded (LL) nodes in proximity. The scheme adopts two criteria, termed as exclusive and common use of VNF instances. Results show that the proposed scheme achieves high migration success at reduced delays, as well as alleviating resources with low migration frequency.

Service Deployment Model Based on Virtual Network Function Resizing

Network function virtualization enables to deploy network services more flexibly with virtual network functions (VNFs). A service provider needs to allocate the required VNFs to hosts, with satisfying different requirements on service delay. It is challenging for service providers to deploy services as efficiently as possible. The previous work has addressed this challenge by studying that multiple services share a VNF instance whose computing capacity is fixed; different VNF instances do not share their computing resources. More efficiency is expected with sharing resources among different VNF instances. This paper proposes a service deployment model for network function virtualization to minimize the deployment cost with capacity sharing and priority queuing. In the proposed model, VNF instances on the same host share the computing capacity in the host by VNF resizing during runtime. In addition, the priority queuing system is applied to each host. We formulate the proposed model as an optimization problem to minimize the service deployment cost. We develop a solution strategy named FlexSize to solve it heuristically in practical time. We evaluate the proposed model with a baseline which does not share the computing capacity among VNF instances in the host. The numerical results show that the proposed model reduces the service deployment cost compared with the baseline.

Modeling and Analysis of Different Reconfiguration Strategies for Virtual Network Function Placement and Chaining with Service Classes Identification

The Virtual Network Function Placement and Chaining problem (VNF-PC) is an important part of the Network Functions Virtualization (NFV) based-technologies implementation. VNF-PC problem focuses on the allocation of customer demands on the Substrate Network. Aiming to evaluate the impact of diverse modeling, various reconfiguration strategies based on implicit steps in solving the VNF-PC are proposed: resizing virtual network instances, re-routing chaining, and repositioning Network Functions (NF) instances on different servers. In addition, this work analyzes, compares, and discusses the advantages and disadvantages of each proposed reconfiguration strategy in an online scenario. Traditionally, VNF-PC solutions from literature process requests generated at random and do not take into account real-world demands. Complementing the analyses of reconfiguration strategies, works from the literature are surveyed to identify commonly used Network Services (NS). Following that, these NS are classified into service classes, and used to generate realistic requests to be mapped in the experimental stage of the reconfiguration approaches. The experiments are conducted using realistic requests and a real-world network topology. An Integer Linear Programming model is used to process the requests. Simulations show that repositioning NF instances can generate up to 25% more profit than reconfiguring only the VNF instances, but the processing time increases by up to 99.99%. On the other hand, resizing virtual network instances and re-routing the chaining had no significant impact on runtime.

Fast Resource Allocation for Resilient Service Coordination in an NFV-Enabled Internet-of-Things System

Network Functions Virtualization (NFV) is a new way of leveraging an Internet-of-Things (IoT) system to provide real-time and highly flexible service creation. In an NFV-enabled Internet-of-Things (NIoT) system, several IoT functions implemented as Virtual Network Functions can be linked as a service function chain to build a customized IoT service quickly. It is important for an IoT service to be able to recover from a failure. However, the supply of a resilient IoT service in an NIoT system is challenging due to the coordination of distributed VNF instances. In this paper, we formulate the problem of resilient service coordination in an NIoT system as a mixed-integer linear programming model, namely RSOd. The model offers the optimal resource allocation for minimizing service disruption when a failure happens at a node of an NIoT system. We also develop two modified versions of RSOd for different use cases required by an IoT provider. Further, two approximation algorithms are proposed to provide a resilient service for a large-scale NIoT system. The evaluation results show that RSOd and its modified versions produce the optimal resource allocation in significantly reduced time compared to previous work. The results suggest that an IoT provider should carefully select an appropriate resource allocation strategy as it has to pay a resource cost to minimize the service disruption. The results also show that our proposed priority-based heuristic algorithm outperforms an approximation algorithm based on Simulated Annealingin terms of the service disruption and computation time.

Анализ моделей управления сетевыми ресурсами в сетях 5G

This article analyzes the existing models of virtual network embedding and dynamic scaling of virtual network resources for Network Slices. These models make it possible to provide services with the required QoS while respecting the concept of efficient network resource usage. Dynamic virtual network embedding models allow virtual networks to be efficiently mapped on a substrate network and reconfigured on demand. But for more flexible dynamic scaling, Holt-Winters, Bi-LSTM and etc. models are additionally used, which are built according algorithms for predicting future resource utilization in order to reduce the initialization time of virtual network function instances serving certain Network Slices. Dynamic scaling models are compared and conclusions about the possibility of their use are given. As a conclusion we were made a summary about the possibility of using these models and the necessity of improvements for more flexible use in 5G networks.