Network Function Virtualization (NFV) has recently attracted considerable attention from both research and industrial communities. Numerous papers have been published regarding solving the resource- allocation problems in NFV, from various perspectives, considering different constraints, and adopting a range of techniques. However, it is difficult to get a clear impression of how to understand and classify different kinds of resource allocation problems in NFV and how to design solutions to solve these problems efficiently. This book addresses these concerns by offering a comprehensive overview and explanation of different resource allocation problems in NFV and presenting efficient solutions to solve them. It covers resource allocation problems in NFV, including an introduction to NFV and QoS parameters modelling as well as related problem definition, formulation and the respective state-of-the-art algorithms. This book allows readers to gain a comprehensive understanding of and deep insights into the resource allocation problems in NFV. It does so by exploring (1) the working principle and architecture of NFV, (2) how to model the Quality of Service (QoS) parameters in NFV services, (3) definition, formulation and analysis of different kinds of resource allocation problems in various NFV scenarios, (4) solutions for solving the resource allocation problem in NFV, and (5) possible future work in the respective area.

With the advent of Network Function Virtualization (NFV), network services that traditionally run on proprietary dedicated hardware can now be realized using Virtual Network Functions (VNFs) that are hosted on general-purpose commodity hardware. This new network paradigm offers a great flexibility to Internet service providers (ISPs) for efficiently operating their networks (collecting network statistics, enforcing management policies, etc.). However, introducing NFV requires an investment to deploy VNFs at certain network nodes (called VNF-nodes), which has to account for practical constraints such as the deployment budget and the VNF-node limited resources. While gradually transitioning to NFV, ISPs face the problem of where to efficiently introduce NFV; here, we measure the efficiency by the amount of traffic that can be served in an NFV-enabled network. This problem is non-trivial as it is composed of two challenging subproblems: 1) placement of VNF-nodes; 2) allocation of the VNF-nodes' resources to network flows. These two subproblems must be jointly considered to satisfy the objective of serving the maximum amount of traffic. We first consider this problem for the one-dimensional setting, where all network flows require one network function, which requires a unit of resource to process a unit of flow. In contrast to most prior work that often neglects either the budget constraint or the resource allocation constraint, we explicitly consider both of them and prove that accounting for them introduces several new challenges. Specifically, we prove that the studied problem is not only NP-hard but also non-submodular. To address these challenges, we introduce a novel relaxation method such that the objective function of the relaxed placement subproblem becomes submodular. Leveraging this useful submodular property, we propose two algorithms that achieve an approximation ratio of $\frac{1}{2}(1-1/e)$ and $\frac{1}{3}(1-1/e)$ for the original non-relaxed problem, respectively. Next, we consider the multi-dimensional setting, where flows can require multiple network functions, which can also require a different amount of each resource to process a unit of flow. To address the new challenges arising from the multi-dimensional setting, we propose a novel two-level relaxation method that allows us to draw a connection to the sequence submodular theory and utilize the property of sequence submodularity along with the primal-dual technique to design two approximation algorithms. Finally, we perform extensive trace-driven simulations to show the effectiveness of the proposed algorithms. While the NFV paradigm offers great flexibility to network operators for efficient management of their networks, VNF instances are typically more prone to error and more vulnerable to security threats compared with dedicated hardware devices. Therefore, the NFV paradigm also poses new challenges concerning failure resilience. That has motivated us to consider robustness with respect to the class of sequence submodular function maximization problem, which has a wide range of applications, including those in the NFV domain. Submodularity is an important property of set functions and has been extensively studied in the literature. It models set functions that exhibit a diminishing returns property, where the marginal value of adding an element to a set decreases as the set expands. This notion has been generalized to considering sequence functions, where the order of adding elements plays a crucial role and determines the function value; the generalized notion is called sequence (or string) submodularity. In this part of the dissertation, we study a new problem of robust sequence submodular maximization with cardinality constraints. The robustness is against the removal of a subset of elements in the selected sequence (e.g., due to malfunctions or adversarial attacks). Compared to robust submodular maximization for set function, new challenges arise when sequence functions are concerned. Specifically, there are multiple definitions of submodularity for sequence functions, which exhibit subtle yet critical differences. Another challenge comes from two directions of monotonicity: forward monotonicity and backward monotonicity, both of which are important to proving performance guarantees. To address these unique challenges, we design two robust greedy algorithms: while one algorithm achieves a constant approximation ratio but is robust only against the removal of a subset of contiguous elements, the other is robust against the removal of an arbitrary subset of the selected elements but requires a stronger assumption and achieves an approximation ratio that depends on the number of the removed elements. Finally, we consider important problems that arise in the production networks, where packets need to pass through an ordered set of network functions called Service Function Chains (SFC) before reaching the destination. We study the following problems: (1) How to find an SFC-constrained shortest path between any pair of nodes? (2) What is the achievable SFC-constrained maximum flow? We propose a transformation of the network graph to minimize the computational complexity of subsequent applications of any shortest path algorithm. Moreover, we formulate the SFC-constrained maximum flow problem as a fractional multicommodity flow problem and develop a combinatorial algorithm for a special case of practical interest.

As Communication Service Providers (CSPs) adopt the Network Function Virtualization (NFV) paradigm they need to transition their network function capacity to a virtualized infrastructure with different Network Functions running on a set of heterogeneous servers. This abstract describes a novel technique for allocating server resources (compute, storage and network) for a given set of Virtual Network Function (VNF) requirements. Our approach helps the telco providers decide the most effective way to run several VNFs on servers with different performance characteristics. Our analysis of prior VNF performance characterization on heterogeneous/different server resource allocations shows that the ability to arbitrarily create many VNFs among different servers' resource allocations leads to a comparative advantage among servers. We propose a VNF resource allocation method called COMPARE that maximizes the total throughput of the system by formulating this resource allocation problem as a comparative advantage problem among heterogeneous servers. There several applications for using the VNF resource allocation from COMPARE including transitioning current Telco deployments to NFV based solutions and providing initial VNF placement for Service Function Chain (SFC) provisioning.

Network Functions Virtualization (NFV) will drive dramatic cost reductions while also accelerating service delivery. Using NFV with SDN, network owners can provision new functions rapidly on demand, improve scalability, and leverage microservices. Benefits like these will make NFV indispensable for service providers, mobile operators, telcos, and enterprises alike. Network Functions Virtualization (NFV) with a Touch of SDN is the first practical introduction to NFV’s fundamental concepts, techniques, and use cases. Written for wide audiences of network engineers, architects, planners, and operators, it assumes no previous knowledge of NFV architecture, deployment, or management. The authors first explain how virtualization, VMs, containers, and related technologies establish the foundation for the NFV transformation. Next, they show how these concepts and technologies can be applied to virtualize network functions in the cloud, data centers, routing, security, and the mobile packet core. You’ll discover new tools and techniques for managing and orchestrating virtualized network devices, and gain new clarity on how SDN and NFV interact and interrelate. By the time you’re done, you’ll be ready to assess vendor claims, evaluate architectures, and plan NFV’s role in your own networks. Understand NFV’s key benefits and market drivers Review how virtualization makes NFV possible Consider key issues associated with NFV network design and deployment Integrate NFV into existing network designs Orchestrate, build, and deploy NFV networks and cloud services Maximize operational efficiency by building more programmable, automated networks Understand how NFV and SDN work together Address security, programmability, performance, and service function chaining Preview evolving concepts that will shape NFV’s future

A horizontal view of newly emerged technologies in the field of network function virtualization (NFV), introducing the open source implementation efforts that bring NFV from design to reality This book explores the newly emerged technique of network function virtualization (NFV) through use cases, architecture, and challenges, as well as standardization and open source implementations. It is the first systematic source of information about cloud technologies' usage in the cellular network, covering the interplay of different technologies, the discussion of different design choices, and its impact on our future cellular network. Network Function Virtualization: Concepts and Applicability in 5G Networks reviews new technologies that enable NFV, such as Software Defined Networks (SDN), network virtualization, and cloud computing. It also provides an in-depth investigation of the most advanced open source initiatives in this area, including OPNFV, Openstack, and Opendaylight. Finally, this book goes beyond literature review and industry survey by describing advanced research topics such as service chaining, VNF orchestrations, and network verification of NFV systems. In addition, this resource: Introduces network function virtualization (NFV) from both industrial and academic perspectives Describes NFV's usage in mobile core networks, which is the essence of 5G implementation Offers readers a deep dive on NFV's enabling techniques such as SDN, virtualization, and cloud computing Network Function Virtualization: Concepts and Applicability in 5G Networks is an ideal book for researchers and university students who want to keep up with the ever-changing world of network function virtualization.

This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2022) held at Institute of Engineering & Management, Kolkata, India, during February 23–25, 2022. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT), and information security.

Mobile traffic had been dramatically increasing in recent years along with the evolution toward next generation of mobile network (5G). To face this increasing demands, Network Function Virtualization (NFV), Software Defined Networking (SDN) and Cloud Computing emerged to provide more flexibility and elasticity for mobile networks toward 5G. However, the design of these softwarization technologies for mobile network is not sufficient by itself as and the mobile services also have critical requirements in term of quality of services and user experiences that still need to be full field. Therefore, this thesis focuses on how to apply efficiently softwarization to mobile network services and associate to it flexible resource allocation. The main objective of this thesis is to propose an architecture leveraging virtualization technologies and cloud computing on legacy mobile network architecture. The proposal not only well adopts and provides flexibility as well as high availability to network infrastructure but also satisfies the quality of services requirements of future mobile services. More specifically, we first studied the use of the "cloud-native" approach and "microservices" for the creation of core network components and those of the radio access network (RAN) toward 5G. Then, in order to maintain a target level of quality of services, we dealt with the problem of the automatic scaling of microservices, via a predictive approach that we propose to avoid degradation of services. It is integrated with an autonomous orchestration platform for mobile network services. Finally, we have also proposed and implemented a multi-level scheduler, which allows both to manage the resources allocated for a virtualized mobile network, called "slice", but also and above all to manage the resources allocated to several network instances, deployed within the same physical infrastructure. All these proposals were implemented and evaluated on a realistic test bench.