Enhancing VNF Instantiation with Blockchain: History
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Contributor:
Anwei Dong
,
Xingwei Wang
,
Bo Yi
,
Qiang He
,
Min Huang

In the realm of Network Function Virtualization (NFV), Virtual Network Functions (VNFs) are crucial software entities that require execution on virtualized hardware infrastructure. Deploying a Service Function Chain (SFC) requires multiple steps for instantiating VNFs to analyze, request, deploy, and monitor resources. It is well recognized that the sharing of infrastructure resources among different VNFs will enhance resource utilization. However, conventional mechanisms for VNF sharing often neglect the interests of both VNF instances and infrastructure providers.

  • NFV
  • VNF
  • resource sharing
  • game model

1. Introduction

Network Function Virtualization (NFV) offers a flexible and scalable approach for the deployment and management of network functions. In the traditional paradigm, network functions were reliant on dedicated hardware devices. However, NFV decouples these functions from specialized hardware, enabling them to operate as software on general-purpose servers [1]. Consequently, various network functions within an IP network can now be configured and managed with increased flexibility and efficiency.
Through the adoption of NFV, network operators and providers gain the ability to easily adjust, upgrade, and introduce new services without being tied to specific hardware dependencies. This adaptability is pivotal for meeting evolving network demands and accommodating increasing traffic. Consequently, integrating NFV with IP networks represents an innovative approach for constructing a more intelligent, flexible, and scalable network infrastructure.
VNFs necessitate execution on virtualized hardware infrastructure, such as virtual machines, containers, or other virtualization platforms, as discussed in a comprehensive review by Kaur et al. [2]. When implementing a Service Function Chain (SFC) within this context, VNFs must go through distinct steps during instantiation.
Requirements Analysis and Planning: This initial phase involves identifying essential VNFs and determining the computing, storage, networking, and other resource requirements for each VNF.
Resource Request: The request for resources, including CPU, memory, and bandwidth, that are necessary for VNFs, must be submitted to the infrastructure or cloud service provider.
Resource Allocation: Resource allocation is carried out based on the submitted resource requests and the availability of resources.
VNF Deployment: After resource allocation, the VNFs are deployed into their designated virtualized instances, such as virtual machines or containers.
Configuration and Optimization: Each VNF undergoes configuration and optimization to ensure the efficient utilization of allocated resources and optimal performance.
Monitoring and Management: Regular and systematic monitoring of VNF performance, resource utilization, and security is a non-negotiable imperative. This enables timely adjustments and optimizations, contributing to the robustness of the entire NFV ecosystem.
In conventional NFV deployments, individual VNFs typically monopolize underlying resources to preempt conflicts. In such instances, service providers deploying service function chains can bypass concerns about interactions among different VNFs, simplifying deployment into a straightforward rental model. However, with the expanding array of network functions, particularly in the context of the burgeoning 5G and evolving 6G networks, the deployment of intricate service function chains has garnered attention. This proliferation has given rise to diverse VNF types, creating a dynamic landscape. Due to fluctuating user demands for various network services at different times, service providers often allocate excess basic resources to ensure a seamless user experience during peak traffic periods. Unfortunately, this practice often leads to the wastage of resources and higher costs. Conversely, infrastructure providers grapple with limitations in managing complex network traffic and providing flexible resource configurations.
Current research in resource sharing [3][4][5][6][7][8][9][10][11][12][13] predominantly concentrates on two key dimensions. The first involves the judicious placement of VNFs, which requires determining the optimal locations and quantities within the network to maintain service quality and improve resource utilization. This encompasses an examination of infrastructure resource sharing among multiple VNFs co-located on the same node, with the overarching goal of achieving efficient infrastructure resource utilization. The second dimension centers on refining service chaining methodologies. This involves carefully scheduling data packets from various business chains on a shared VNF, enabling efficient resource sharing and promoting VNF sharing. Nevertheless, these methodologies often neglect the inherent interests among different VNF entities and infrastructure providers. As the adage goes, “all’s fair in love and war.” Devoid of appropriate incentives, even the most adept resource-sharing strategies pose implementation challenges. Hence, it becomes imperative to incorporate economic incentives into the VNF instantiation phase to maximize the benefits for both infrastructure providers and VNF entities involved in resource sharing.
Access control is a critical component in NFV, which function as a pivotal mechanism for safeguarding sensitive data and resources. This mechanism involves various operations that include different subjects, such as users, roles, services, etc. Its significance lies in its ability to regulate access to distinct objects, including files, devices, and services, while concurrently ensuring the integrity, confidentiality, and availability of resources [14]. In multi-tenant environments that share common infrastructure and resources, access control policies can be vulnerable to inconsistencies and conflicts. This can amplify concerns regarding resource isolation and protection. Within such contexts, the presence of malicious or unauthorized entities poses a looming threat, with the potential to compromise sensitive data, disrupt normal service operations, and instigate severe consequences. These consequences can range from data breaches to service interruptions and performance degradation.
Prior investigations in the field of NFV resource sharing have notably neglected the examination of robust access control strategies, despite their pivotal significance. Other research on access control [15][16][17][18] primarily focuses on Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Policy-Based Access Control (PBAC). To ensure precise subject identification and verification within the NFV system, trusted identity management and authentication mechanisms are commonly deployed. These mechanisms utilize various tools, such as digital certificates, tokens, passwords, and similar techniques, as illustrated in Figure 1. Nevertheless, in many conventional approaches, it is common to rely on third-party involvement for key distribution. If the third party lacks trustworthiness, data security is severely compromised.
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Figure 1. Traditional access control model.
Blockchain is a decentralized and distributed ledger technology that enables secure and transparent record-keeping of transactions across a network. Each transaction, or “block”, is linked to the previous one through a cryptographic hash, which forms a chain of blocks. This immutability and consensus mechanism makes it extremely resistant to tampering or unauthorized alterations. Blockchain is most commonly associated with cryptocurrencies such as Bitcoin, where it serves as the underlying technology for a secure and decentralized financial system. However, its applications extend beyond finance to various industries, including supply chain management, healthcare, and smart contracts, offering enhanced transparency, traceability, and trust in digital transactions [19][20][21][22][23][24]. Notably, recent studies have explored the utilization of blockchain technology in fields associated with the placement, addressing, and resource allocation of VNFs [25][26][27][28].
Game theory, a mathematical discipline [29], delves into the strategic interactions among decision makers, commonly referred to as players, across diverse scenarios. This branch involves analyzing the choices made by players, known as strategies, and gaining a comprehensive understanding of the resulting outcomes and impacts on each participant. Whether in the realm of economic competition, political decision making, or biological interactions, game theory offers a versatile framework for examining situations wherein individuals or entities navigate decisions within dynamic and interactive environments. Its applications traverse disciplinary boundaries, providing valuable insights into cooperation, competition, and the intricate interplay of decisions within human and natural systems. Notably, in recent years, game theory has been frequently applied to delineate VNF chains and formulate strategies for the allocation of VNF resources [30][31][32][33][34][35].

2. Tthe Strategic Placement of VNFs

According to the findings of the literature survey, the strategic placement of VNFs encompasses a multifaceted approach. This entails a thorough analysis of the necessary resources, the formulation of strategies for resource allocation, the resolution of conflicts pertaining to resource sharing, the optimization of performance, the balancing of workloads, the dynamic adjustment of resources, and the implementation of secure isolation mechanisms. These efforts are all directed towards achieving the twin objectives of maximizing resource utilization and fulfilling network functionality requirements. Huang et al. [3] introduced AutoVNF, an automated mechanism for optimizing VNF deployment. This mechanism incorporated a resource-sharing mode and an automated resource allocation mechanism, which effectively support multiple VNFs sharing resources on a single node and dynamically allocating available nodes to VNF requests. Cohen et al. [4] proposed two approximate algorithms to address the VNF placement problem. One for cases without capacity constraints and another for cases with capacity constraints. The primary objectives of these algorithms were to minimize the distance between users and service nodes and to reduce the deployment cost of VNFs. Sun et al. [5] presented a method for optimizing the placement of VNFs. The method took into account the resource sharing among VNFs and the stochastic characteristics of Poisson arrival traffic. This approach utilized a queuing model to examine queueing delays within the VNF queue, thereby formulating the VNF placement problem as a 0–1 quadratic fractional programming challenge. This method addressed the complexities of balancing service quality and placement expenses across diverse traffic categories in resource-sharing VNFs. Savi et al. [6] introduced a method that leverages Integer Linear Programming (ILP) and Heuristic Computing Algorithm (HCA) for optimizing VNF placement and SFC embedding. This approach considered the performance loss due to the sharing of processing resources in a multi-core CPU architecture, which includes the associated cost increase and context-switching overhead. The main objective of this approach was to minimize the number of activated NFV nodes, thereby reducing the implementation cost associated with NFV.
With the widespread adoption of machine learning [36], numerous studies have been proposed to achieve intelligent VNF mapping. In their work, Sun et al. [7] advocated for a dynamic resource allocation scheme grounded in VNFs. This scheme leveraged online learning techniques to forecast user mobility patterns and allocate resources according to the heat generated by base stations. The authors introduced a supplementary mechanism that reallocates idle resources from demand-surplus base stations to demand-deficient ones. This mechanism prioritized the requirements of the latter group. Mu et al. [8] presented an approach based on Deep Reinforcement Learning (DRL) to optimize VNF placement. This study adopted a holistic approach to address the issue of data center server energy consumption and performance interference among VNFs. The objective is to minimize the overall server energy consumption while ensuring that the performance of each VNF exceeds a predetermined threshold. Basu et al. [9] proposed a machine learning-based methodology that integrates SDN and NFV to realize dynamic resource sharing in 5G-assisted unmanned aerial vehicle networks. The approach employed two regression models, Support Vector Regression and Kernel Ridge Regression, to predict VNF resource requirements and dynamically allocate VNFs based on the prediction results.
VNF sharing entails the optimal utilization of a singular VNF instance to handle multiple service requests that necessitate the same category of VNF. It is also applicable in cases where a specific VNF type needs to be deployed in multiple instances to meet the requirements of a particular service. Within VNF sharing, resources assigned to a VNF specific instance are concurrently utilized by multiple data packets, thereby diminishing packet waiting times in the queue. Li et al. [10] proposed a method tailored for deploying VNFs in data centers and introduced innovative techniques such as shared redundancy and multi-tenancy. This led to the development of a Joint Deployment and Backup Scheme (JDBS). The JDBS dynamically adjusted VNF deployment and backup strategies iteratively to effectively balance Basic Resource Consumption (BRC) and Shared Redundancy Consumption (DRC), ultimately achieving an optimal equilibrium between the two. Vieira et al. [11] considered the dynamic characteristics of edge environments, incorporating factors such as resource availability, uncertainty in user requests, QoS requirements, and user mobility. They employed a time window strategy to process batches of continuously arriving service requests. The algorithm also presented a two-tier resource-sharing mechanism, which facilitates the sharing of VNF instances or SFC instances among multiple services to reduce resource consumption and associated costs. Ruiz et al. [12] introduced a Genetic Algorithm-based approach to jointly addressed VNF placement, VNF chaining, and virtual topology design. The authors leveraged collaborative capabilities among Multi-access Edge Computing (MEC) nodes to enable VNF sharing. This approach utilized a novel search strategy during the chaining process, which prioritizes the identification of available VNFs in both local nodes and Central Offices. In the absence of such resources, the search extended to the physically nearest node within the topology until all network nodes have been explored. Yi et al. [13] proposed a dynamic and flexible algorithm to address VNF shared resource allocation and rate coordination between upstream and downstream VNFs. Specifically, the algorithm considered fairness factors during VNF sharing to reduce the probability of resource contention and enhance resource utilization. Additionally, by defining a backpressure indicator for each VNF to assess its pressure status, it dynamically adjusted the processing rate between the VNF and its upstream and downstream VNFs, with the aim of optimizing the utilization of idle resources.
The study by Kumar et al. [15] offered a comprehensive examination of security concerns and resolutions pertaining to VNF within the telecommunication domain. The paper systematically analyzed potential security threats and attacks targeting various components and layers within the NFV architecture. The proposed security measures for VNFs encompass aspects such as security hardening, role-based access control, software integrity, and protection against malicious code. Gui et al. [16] presented a distinct identity and access control scheme tailored for microservices in 5G platforms, which relys on OpenID Connect and JSON Web Tokens. This scheme facilitated the authentication and authorization processes for both users and microservices, thereby enhancing the overall lifecycle management of virtualized services. A notable feature of this study resided in its practical application and comprehensive evaluation carried out within the context of the SONATA service platform. Simultaneously, Smine et al. [17] proposed an innovative approach for the correct and optimal deployment of access control policies in NFV services. The approach considered a robust insider adversary model capable of compromising one or multiple VNFs within the Management and Orchestration (MANO) framework. Furthermore, Murillo et al. [18] introduced a specialized access control framework for virtualized Industrial Control Systems (ICS). The framework incorporated an advanced policy language to clearly define the components, roles, and authorized operations within the ICS. Additionally, the system included a policy engine that facilitated the translation of high-level policies into low-level rules, enabling their execution across various virtualization platforms. The primary objective of this framework was to furnish ICS administrators with a user-friendly tool for flexibly defining and managing access control policies in virtualized ICS.
In light of the preceding analysis, contemporary research initiatives in the field of VNF resource sharing primarily focused on traffic attributes and the succession of service supply chains. Unfortunately, these efforts often neglect the crucial issue of guaranteeing a fair and just allocation of benefits among the diverse entities engaged in resource sharing. Regarding resource access control, the pertinent literature predominantly centered on enhancing extant models based on third-party authentication.
Moreover, there has been some related works on the application of blockchain in the placement and resource allocation of VNFs. Liu et al. [25] presented a blockchain-based approach that incorporates vector commitments and Succinct Non-Interactive Knowledge Proof (SNARK) techniques for VNF management. Their proposed method efficiently managed VNF dictionaries and validates queries. Taskou et al. [26] proposed a blockchain-based strategy for NFV resource allocation. Through the use of smart contracts, their approach achieved decentralized, secure, and reliable resource allocation. The paper defined two optimization problems: the NFV resource allocation problem, which aims to minimize energy consumption and resource costs for data centers, and the mining task offloading problem, which seeks to minimize energy consumption for mining users. Papadakis et al. [27] introduced a blockchain-based network service marketplace and resource orchestration mechanism to enable cross-service communication within the edge cloud. Leveraging the smart contract functionality of the Hyperledger Fabric platform, the paper automated network service interactions and lifecycle management among different tenants. Additionally, it introduced an innovative service orchestrator that utilizes the capabilities of Open Source MANO (OSM), establishing cross-service communication with minimal resource requirements and instantiation costs. Regarding the allocation and competition strategies for VNF resources, Franco et al. [28] utilized blockchain and smart contract technologies to propose a reverse auction-based solution for discovering and selecting infrastructure capable of efficiently hosting VNFs. This solution encouraged competition among Infrastructure Providers, thereby mitigating the deployment costs for VNFs while simultaneously addressing the unique needs of users. Notably, the solution leveraged the tamper-proof and auditable features of blockchain, which ensures reliable records and contract execution. An advantageous aspect of this solution was its consideration of various user and VNF requirements, such as minimum resources, geographical location, and maximum latency, rather than relying solely on pricing for infrastructure selection.
Moreover, existing literature has delved into the utilization of game theory to delineate VNF chains and formulate strategies for the allocation of VNF resources. Leivadeas et al. [30] presented an approach grounded in graph partitioning game theory to address the placement problem of VNF service chains. The method effectively implemented service chains in cloud environments. The achievement was made possible by effectively addressing server affinity, coexistence, and latency constraints. Simultaneously, the method aimed to minimize deployment costs while also achieving resource load balancing. Chen et al. [31] introduced an incentive-driven framework for VNF chains, aiming to optimize resource allocation across different layers, such as bandwidth and IT resources. This framework was specifically designed for Interconnected Data Center Elastic Optical Networks (IDC-EONs) and involved coordination among multiple agents. The framework employed a non-cooperative hierarchical game theory mechanism, where resource agents assume the role of leaders and VNF-SC users act as followers. Within the leader game, agents calculated VNF-SC service solutions for users and calculated them for configuration tasks. In the follower game, users competed for cross-layer resources based on the service solutions provided by agents, aiming to achieve a joint optimization of resource cost and service quality. Gao et al. (2022) [32] introduced a VNF placement by potential games. The objective of the method was to enhance resource allocation and improve service quality in the context of satellite edge computing. The approach modeled the VNF placement problem as a non-cooperative potential game and utilized the Nash equilibrium as the solution concept. Le et al. [33] employed a game-theoretic approach, coupled with the semi-tensor product matrix tool, to investigate the SFC routing problem. The consideration encompassed both limitations in server capacity and constraints on the minimum target rate for users. This method effectively ensured NFV server capacity constraints while meeting user rate requirements. Li et al. [34] utilized a game-theoretic approach to address the problem of embedding multiple SFCs. The methodology considered both the impact of resource sharing among different VNFs and the limitations in capacity of various NFV nodes. The objective of this approach was to minimize the end-to-end (E2E) latency for the traffic supported by each SFC while satisfying the capacity constraints of all NFV nodes. Regarding the resource allocation mechanism for VNFs, Lima et al. [35] proposed a approach to address the resource management and orchestration problem in NFV. The mechanism utilized a bilateral sealed-bid auction model, which treats users and infrastructure providers as buyers and sellers, respectively. It employed a centralized agent to match demands and bids, resulting in the optimization of the social welfare for both buyers and sellers.

This entry is adapted from the peer-reviewed paper 10.3390/s23239343

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