A Multi-Domain VNE Algorithm Based on Load Balancing in the IoT Networks

Network softwarization technologies, such as software defined networking (SDN), network virtualization (NV), and network function virtualization (NFV), emerge and have been greatly promoted by their supporters. This softwarization scheme contributes to isolating network control from data panel, and allocating virtualized physical network resources flexibly. However, physical switches and links, supporting the softwarization scheme, may be attacked by malicious attackers. Even if these switches and links are softwarized and virtualized successfully, the problem of mapping various virtual network requests, known as virtual network embedding, is difficult to solve directly. Each customized VN request must be allocated and implemented in the appropriate manner. To deal with these issues, we research the virtual network mapping for secure SDN-enabled networks. Firstly, a hierarchical virtualization architecture for SDN- enabled networks is presented. Then, the security model for different malicious attacks is presented. Thirdly, we propose the novel and secure virtual network mapping framework, labeled as \textit{NoViSec}, for supporting SDN-enabled networks. Preliminary experiment results are plotted and discussed so as to validate the effectiveness of our secure \textit{NoViSec} framework.

Aiming at the virtual network embedding problem under dynamic service request, Service Request Aware-based Dynamic Virtual Network Embedding (SRAD) algorithm is proposed. We first present virtual network embedding model, and then define virtual network construction cost according to the dynamic characteristic of service request. Our goal is to find the optimal reconfiguration policies by awaring service request type and changing trends, which can minimize the overall construction cost using substrate resource. Experimental results show that the proposed algorithm satisfies the service request, achieves higher success ratio and gains higher revenue/cost ratio for substrate network comparing with the existing algorithms.

Software middlebox-based services can be flexibly managed by software defined networking (SDN) and network function virtualization (NFV). Meanwhile, traffic routing can be simplified and the number of routing rules in the SDN-enabled switches can be reduced through the consolidated middlebox model. However, different network functions in middleboxes may alter the volume of processed traffic, so high congestion may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planned. Besides, in a statically switch-controller configured SDN, traffic dynamics will not only affect the link load in the data plane, but also pose a challenge to controller load balancing. Therefore, it’s necessary to achieve better quality-of-service (QoS) performance in both control and data planes. In this article, we first formulate this problem as a joint traffic-aware consolidated middleboxes selection and routing (JTMSR) problem and prove its NP-hardness. Then, we design a two-phase algorithm to achieve the controller and link load balancing where the first phase is to redirect selected flows by applying wildcard rules and the second phase is to find fine-grained routing path by a rounding-based algorithm with bounded approximation factor. Finally, compared with the existing algorithms through extensive simulations, it demonstrates that our method has near-optimal controller load balancing and link load balancing performance and can improve response time by 9.7% compared with static scheme.

5G networks represent a paradigm shift from their 4G predecessors, engineered to cater to the burgeoning demands of densely populated areas and the high data throughput requirements of the Internet of Things (IoT) and smart city infrastructures. Beyond enhancing fundamental network attributes such as reliability, availability, and security, 5G introduces architectural innovations to accommodate these advanced needs. This paper delves into the architectural nuances of 5G, focusing primarily on the Standalone (SA) deployment method, distinguished by its utilization of network slicing. This approach leverages cutting-edge virtual network technologies, including Software-Defined Networking (SDN) and Network Function Virtualization (NFV), to forge virtual network slices tailored to diverse consumer needs. Such customization is imperative for meeting the expansive and varied connectivity demands characteristic of 5G networks.To navigate the complexities of 5G architecture, this study advocates for the adoption of Reference Architectures (RAs), which provide a high-level abstraction framework for dissecting and understanding intricate systems. By proposing a model that outlines the standalone architecture of 5G networks, our research lays the groundwork for evolving this model into a comprehensive RA for 5G. This foundational work is instrumental in equipping system designers with the tools necessary for generalizing systems to manage design and deployment challenges effectively, as well as the heterogeneity inherent in system components. Additionally, our proposed model facilitates the establishment of a common vocabulary, promoting a coherent understanding of component interactions and offering a unified system perspective. Ultimately, this paper contributes a pivotal model that not only enhances our comprehension of 5G network architecture but also paves the way for future advancements in network design and deployment, promising to significantly impact the development of smart cities and IoT ecosystems.Keywords: 5G, 5G architecture, Reference architecture, patterns. Network slicing, Network Function Virtualization, Virtual Network Function.

The design, management, and operation of network infrastructure have evolved during the last few years, leveraging on innovative technologies and architectures. With such a huge trend, due to the flexibility and significant economic potential of these technologies, software-defined networking (SDN) and network functions virtualization (NFV) are emerging as the most indispensable key catalysers. SDN/NFV enhancing the infrastructure agility, thus network operators and service providers are able to program their own network functions (e.g., gateways, routers, load balancers) on vendor-independent hardware substrate. One of the most important considerations in NFV deployment is how to allocate the virtual resources that are needed to provide flexible virtual network services in an NFV-based network infrastructure. Thus, the most important prerequisite for NFV deployment is achieved fast, scalable and dynamic composition and allocation of networks functions (NFs) to implement network services (NSs). We have proposed a revised RA algorithm that integrates embedding and scheduling of virtual network functions (VNFs) simultaneously. In this paper, performance evaluation of the revised RA algorithm is performed and the proposed algorithm is applied more effectively in NFV environment where the demand for resources is flexible and concentrated.

Network availability and service continuity are major concerns for network operators to provide reliable communication services for Internet of Things (IoT), which are particularly challenging to achieve in virtualized network slicing environment where network services are exposed to the failure risks of both software (virtual network function (VNF) instances) and hardware (physical nodes). In general, the redundancy-based VNF backup solutions are used to improve the reliability of virtualized network slices. However, backup VNFs require the same amount of resources as the primary VNFs, which will result in high-resource cost. In this article, we propose a joint VNF partition and hybrid backup scheme for VNF orchestration, backup and mapping, whose aim is to construct the reliability-enhanced and delay-guaranteed network slices at minimum cost. Specifically, the VNF partition method divides a single VNF into multiple thinner VNFs with lower processing capacity and is expected to enhance the reliability of network slices with less additional resources. The hybrid backup scheme includes both onsite and offsite backup forms. Then, considering the time-varying network environment and IoT service requirements, we formulate the VNF orchestration, backup and mapping as a dynamic mixed integer linear programming (DMILP) problem, and model the dynamic problem as a Markov decision process (MDP). In view of the large action space of the formulated MDP, we propose a multiagent deep reinforcement learning (DRL) approach with an action space reduction strategy to achieve the dynamic VNF orchestration, backup and mapping solution. Simulation results demonstrate that the proposed joint VNF partition and hybrid backup scheme can obtain superior delay and reliability performance with low-network cost.

Internet of Things (IoT) enables intelligent services varying with the complex and realtime environment to achieve network benefits, where Network Function Virtualization (NFV) can dynamically provide Virtualized Network Functions (VNFs) for IoT devices. In NFV-enabled IoT architecture, a service function chain (SFC) consists of an ordered set of virtual network functions (VNFs). However, the energy consumption of the VNF migration and SFC reconfiguration is one major issue owing to the dynamic characteristic of IoT network. In this paper, we propose a new paradigm digital twin (DT) to create the virtual twin of physical objects in IoT network, then, we formalize the problem as a mathematical model, which aims to minimize the energy consumption. To this end, we prove this problem is NP-hard and propose an algorithm Bidirectional Gated Recurrent Unit (Bi-GRU) based on federated learning to predict the resource requirement. Further more, according to the prediction result, which utilizing the deep reinforcement learning (DRL) algorithm for decision making of the VNF migration. Simulation results show that our proposed method can effectively reduce the number of VNFs to be migrated and economize the energy consumption of the digital twin IoT network.

Network functions virtualization (NFV) technology is an emerging networking technology in the telecom industry which provides flexible deployment of network services by reducing the Operating Expenses and Capital Expenses. NFV is an innovative step to virtualize network services that usually run on fixed hardware. With the concept of NFV, services like routing, load balancing, deep packet inspectors and firewalls are packaged as virtual network functions (VNFs) on commodity hardware. NFV helps the service provider to flexibly add that service in the form of a virtual machine (VMs) without upgrading or buying a new hardware on the customer end. This allows a faster deployment of the all the network services with new innovations and helps in increasing great opportunities in the field of telecom-networking. This paper provides an information about NFV technology with its characteristics, enabling technologies, benefits, use cases and challenges. NFV architecture and its comparison with Software Defined Networking (SDN) technology is also presented in this survey paper.

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.

The cloud offers more services across multiple infrastructures and rapidly growing areas of development in medical care. In the cloud, Network virtualization allows multiple isolated virtual networks (VNs) for flexible sharing of network resources. The virtual network mapping in Network virtualization provides the dynamic virtual node and link resources to satisfy the user needs. The major challenges of cloud computing are optimally and resourcefully responds to each user service requests with minimum time. To address these problems in distributed and hybrid cloud environments, Multicriteria Dragonfly based Graph Theory Resource Optimized Virtual Network Mapping (MD-GTROVNM) technique is introduced. The main objective of the MD-GTROVNM technique is to improve the efficiency of virtual network request mapping with less resource utilization. In the MD-GTROVNM technique, Multicriteria Dragonfly based Graph Theory performs both virtual node mapping and link mapping with reasonable resource utilization such as CPU, memory, and bandwidth. In node mapping, the Multicriteria Dragonfly optimization technique is applied to find the optimal physical node among the population that satisfies the resource constraints. The proposed Multicriteria Dragonfly optimization algorithm achieved a more optimal solution for virtual network mapping. The experimental scenario is carried out with various parameters such as mapping efficiency, computation time, Request acceptance ratio and memory consumption with a number of VN requests. The observed results confirm that the MD-GTROVNM technique effectively increases the mapping efficiency, Request acceptance ratio and minimizes the computation time as well as memory consumption when compared to the existing techniques.

The Internet of Things (IoT) brings a multidisciplinary revolution in several application areas. However, security and privacy concerns are undermining a reliable and resilient broad-scale deployment of IoT-enabled critical infrastructures (IoT-CIs). To fill this gap, this paper proposes a comprehensive architectural design that captures the main security and privacy challenges related to cyber-physical systems and IoT-CIs. The architecture is devised to empower IoT systems and networks to make autonomous security decisions through the usage of novel technologies such as software defined networking and network function virtualization, as well as endowing them with intelligent and dynamic security reaction capabilities by relying on monitoring methodologies and cyber-situational tools. The architecture has been successfully implemented and evaluated in the scope of ANASTACIA H2020 EU research project.

In the software defined optical network (SDON) for virtual network services, there still exists unbalanced traffic load distribution in SDON. The traditional load balance mapping method exists in the problem of heavy load of key nodes in optical network and low request acceptance rate of virtual network. In this paper, a virtual mapping method of optical network based on topology awareness is proposed. When the controller of optical network is defined by software, the topology information of nodes and links in the whole network is acquired, and the relative balance weight of each node and link is calculated by combining the connectivity factor. When the virtual network service arrives, the optical network is based on the bandwidth of virtual network service the method of node mapping and link mapping is needed to improve the success rate and balance of virtual network mapping, and avoid the local overload of optical network resources.

Joint optimization of Service Chain Graph Design and Mapping in NFV-enabled networks

Routing Protocol for Low power lossy network (RPL) is emerged to evade the energy issues in the Internet of Things (IoT) network. Although, RPL noticeably satisfies the requirement of the IoT network, still there exist certain open issues to resolve. This paper addresses the problem of routing overhead, packet losses and load imbalance in RPL based IoT network. These downsides are solved using the proposed Dual Context‐based Routing and Load Balancing in RPL based Network (DCRL‐RPL). Primarily, we implement the grid construction where the network area is split into a different level of the unequal grid. The ranking based Grid Selection process is executed to select the optimal grid head node in each grid. The grid head node in the network schedules its member node using the Reputation based Scheduling method. The elected grid head classifies the data received from its member using Adam Deep Neural Network (ADNN) to provide better routing performance. Here, the sensitive and non‐sensitive data are considered as context for objective function selection. At last, the efficacy of the DCRL‐RPL is analyzed using the Network Simulator 3.26 (NS3) tool. From comprehensive validation results, we show that DCRL‐RPL acquires better results.

Internet adoption is increasing at the fastest rate ever driven by new mobile edge users. To meet these added demands from network services efficiently, telecom service providers are exploring virtualization technologies. NFV architecture broke the dependency on commodity hardware by decoupling network services from legacy hardware devices and implementing network functions (load balancer, firewall) in virtual machines. Multiple virtualization technologies including VMs, containers, unikernels, and kata-containers are being explored to deploy network functions. Each one of them faces a trade-off between isolation and agility. Performance analysis is especially important in upcoming Internet Of Things (IOT) and edge computing where quick responses are expected in a resource-scarce isolated environment. Containers shared kernel raises security concerns and kata containers addresses that by providing a secure light weight VM to run container. We present for the first time kata containers in respect to Network Function Virtualization (NFV). This paper studies the performance analysis of NFV virtualization technologies by deploying web services and provides descriptive results based on output. Kata provides a fast light weight VM to securely run containers but with 20-30% added overhead compared to docker containers. Unikernels are secure, light weight and are well suited to run applications with high context.