Optimized Resource Allocation for Cloud-Native 6G Networks: Zero-Touch ML Models in Microservices-Based VNF Deployments

The discovery process of OPC Unified Architecture (OPC-UA) is well defined but lacks flexibility when implemented in large networks. While the standard provides concepts for local and global discovery services, the core assumption is that all clients and servers are aware of the discovery server(s) in the network. This dependency is a barrier for Plug-and-Play installations and is overcome by self announces via the multicast Domain Name System (mDNS), however, this scales badly with an increasing number of network nodes and deployed servers. We propose a discovery approach based on Software-Defined Networking (SDN) and Network Function Virtualization (NFV), that installs a plug-and-play mechanism for global OPC-UA discovery in modern virtualized networks.

Network Function Virtualization (NFV) has promoted the implementation of network functions from expensive hardwares to software middleboxes. These software middleboxes, also called Virtual Network Functions (VNFs), are executed on switch-connected servers. Efficiently deploying such VNFs on servers is challenging because the traffic rate of flows must be fully processed by their requested VNFs when they reach destinations, and the deployed positions of VNFs are restricted by the server capacity. In addition, each network function offers non-uniform VNF models (types) with different configurations of processing volumes and costs. This paper focuses on minimizing the total cost of deploying VNFs for providing a specific network function to all flows in tree-structured networks. First, we prove the NP-hardness of non-uniform VNF deployment in a tree topology and propose a dynamic programming based solution with a pseudo-polynomial time complexity. Then we narrow it down to three simplified cases by focusing on either uniform VNFs or the linear line topology. Specifically, three algorithms are introduced: an improved dynamic programming based algorithm for deploying uniform VNFs in a tree topology, a performance-guaranteed algorithm for deploying non-uniform VNFs in a linear line topology, and an optimal greedy algorithm for deploying uniform VNFs in a linear line topology. Additionally, we generalize our approach to a case of deploying a service chain, which consists of multiple network functions applied to flows in a specific order. We propose two solutions: one is optimal but time-consuming while another is heuristic but efficient. Extensive simulations are conducted to evaluate our algorithms.

Diverse proprietary network appliances increase both the capital and operational expense of service providers, meanwhile causing problems of network ossification. Network function virtualization (NFV) is proposed to address these issues by implementing network functions as pure software on commodity and general hardware. NFV allows flexible provisioning, deployment, and centralized management of virtual network functions. Integrated with SDN, the software-defined NFV architecture further offers agile traffic steering and joint optimization of network functions and resources. This architecture benefits a wide range of applications (e.g., service chaining) and is becoming the dominant form of NFV. In this survey, we present a thorough investigation of the development of NFV under the software-defined NFV architecture, with an emphasis on service chaining as its application. We first introduce the software-defined NFV architecture as the state of the art of NFV and present relationships between NFV and SDN. Then, we provide a historic view of the involvement from middlebox to NFV. Finally, we introduce significant challenges and relevant solutions of NFV, and discuss its future research directions by different application domains.

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.

Software defined networking (SDN) and network function virtualization have emerged as a promising solution for elastic, dynamic, and scalable network management. A collection of works have investigated how virtualization and cloud technology can ameliorate the infrastructure management for the fifth generation mobile network. However, employing these techniques in mobile network for live streaming has not received enough attention. By leveraging the development of SDN and virtualization techniques, mobile live streaming service providers can efficiently manage their system to cope with network dynamics incurred by the variation of the environment. Specifically, we dynamically instantiate network entities at appropriate locations in response to the user live streaming demands. These network entities are in charge of transcoding and transmitting the live videos to the mobile end users, which we name as virtual live streaming agent (vLA). In this paper, we investigate the dynamic virtualization and migration of vLAs for mobile live streaming services. Typically, rate adaptive streaming is adopted to improve spectrum efficiency and user quality of experience. We formulate an integer linear program for the optimal vLA placement problem. Furthermore, we design a practical SDN-based live vLA migration process. Then, by designing and integrating migration cost functions, we develop a dynamic vLA placement mechanism for dynamic environments with the consideration of migration cost. Both heuristic and on-line algorithms are designed for the vLA placement and migration problems. Finally, a large scale real trace-based simulation is conducted to demonstrate the performance of our vLA placement and migration algorithms.

Attack-aware Security Function Management

The integration of software-defined networking (S-DN) and network function virtualization (NFV) is expected to achieve the convenient management and flexible deployment of various sophisticated network functions, and support user applications with guaranteed quality of service (QoS). In this paper, we jointly study route selection and network function placement problem. To stress the service sensitivity on delay, we formulate an optimization problem which minimizes the total end-to-end delay subject to data transmission, service requirement and various available resource constraints. As the formulated problem is an NP-hard problem, which cannot be solved easily, we transform it into three subproblems, i.e., route selection subproblem, network function placement subproblem and resource sharing subproblem of user flows, and solve the three subproblems by applying the K-shortest paths algorithm, Kuhn-Munkres (K-M) algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.

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.

It is our great pleasure to welcome you to the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization - SDN-NFV Security'18. SDN-NFV Security'18 offers a unique opportunity by bringing together researchers from academic, industry, as well as government agencies to discuss security challenges faced by two emerging networking paradigms, Software-Defined Networking (SDN) and Network Function Virtualization (NFV). SDN-NFV Security'18 solicits the submission of original unpublished ideas in 6-page long papers and 4-page short papers that address the latest practices, experiences, and lessons learned on SDN and NFV security. The call for papers attracted submissions from Europe and the United States. SDN-NFV Security'18 accepted 3 full papers be presented in this workshop, after a rigorous review process (at least 4 reviews for each paper) and discussion phase. In addition, we have invited 8 papers (5 full papers and 3 short papers) from domain experts to be presented in the workshop.

Fault tolerant placement of stateful VNFs and dynamic fault recovery in cloud networks

Metropolitan networks are undergoing a major technological breakthrough leveraging the capabilities of software-defined networking (SDN) and network function virtualization (NFV). NFV permits the deployment of virtualized network functions (VNFs) on commodity hardware appliances which can be combined with SDN flexibility and programmability of the network infrastructure. SDN/NFV-enabled networks require decision-making in two time scales: short-term online resource allocation and mid-to-long term offline planning. In this paper, we first tackle the dimensioning of SDN/NFV-enabled metropolitan networks paying special attention to the role that latency plays in the capacity planning. We focus on a specific use-case: the metropolitan network that covers the Murcia - Alicante Spanish regions. Then, we propose a latency-aware multilayer service-chain allocation (LA-ML-SCA) algorithm to explore a range of maximum latency requirements and their impact on the resources for dimensioning the metropolitan network. We observe that design costs increase for low latency requirements as more data center facilities need to be spread to get closer to the network edge, reducing the economies of scale on the IT infrastructure. Subsequently, we review our recent joint computation of multi-site VNF placement and multilayer resource allocation in the deployment of a network service in a metro network. Specifically, a set of subroutines contained in LA-ML-SCA are experimentally validated in a network optimization-as-a-service architecture that assists an Open-Source MANO instance, virtual infrastructure managers and WAN controllers in a metro network test-bed.

Network function virtualization (NFV) technology is widely used in network slicing in 5G networks and traffic processing in wide area network. However, with the increasing of service requests during the life cycle of a virtual network function (VNF), how to flexibly deploy the VNF becomes a key problem to make maximum use of the limited capacity of the physical network resources, and meanwhile satisfy the requirements of quality of service (QoS) in NFV scenario. In this paper, aiming to solve whether and how to scale VNF on demand, we formulated this problem as a non-convex linear mathematical optimization model where the optimization goal is to minimize the delay and energy consumption of the service function chain (SFC). Specifically, we propose a VNF flexible deployment scheme based on Reinforcement Learning (RL). Moreover, we train the agent by interacting with the physical network environment and take action according to the state of physical node to find the optimal physical resource allocation strategy of the VNF scaling. In addition, the state space, action space and the reward function are defined as available resource, migration or scaling decision and the reciprocal of total cost respectively. Extensive simulation results demonstrate that the proposed algorithm outperforms the comparison algorithm in terms of reducing the delay and increasing the ratio of successful scaling request.

Middleboxes have become an integral part of modern enterprise and data center networks. They are used for realizing various performance and security objectives. Most middlboxes (e.g., firewalls, Intrusion Detection Systems (IDSs), Network Address Translators (NATs), etc.) are dedicated hardware appliances. However, recent advancements in cloud and virtualization technologies have fueled the concept of Virtual Middleboxes or Virtual Network Functions (VNFs) along with a new research field known as Network Function Virtualization (NFV). This area of research has gained a lot of traction from both industry and academia. Although much progress has been made in NFV technology, a crucial component for realizing the primary objective of NFV is still missing – a management and orchestration [7] system that conforms to the principles of NFV: open source, open API and standardized software solutions. Without this feature, network operators may end-up with the same situation of vendor lock-in as with proprietary hardware middleboxes. A number of recent proposals like Stratos [8], OpenNF [9], and Split/Merge [10], strive to fulfill the requirements for VNF management and orchestration. However, they propose incompatible northbound APIs. What is really needed is a standardized API that is flexible enough to express a wide range of NFV management and orchestration operations. History shows that standardization efforts usually take a long time and often are futile. Hence, we take a different approach, and propose to use an existing, well known, standardized interface for NFV management and orchestration: the Linux file system interface.

The evolution of interconnected systems and the evolving demands in service requirements have led to data centers integrating multiple heterogeneous technologies that must coexist. Consequently, the resource management and the security of the infrastructure are becoming more complex than in traditional scenarios. In this context, technologies such as Software-Defined Networking (SDN) or Network Function Virtualization (NFV) are being embraced as mechanisms that facilitate communication management. The integration of both technologies into a single framework, termed Software-Defined NFV (SDNFV) introduces a multitude of tools for managing the security of the data center’s resources. This work delineates the primary characteristics of the evolution of these communication networks and their application to information security and communications within a data center. It presents an illustrative use case demonstrating the application of these next-generation technologies to detect and mitigate a security issue through virtualized network functions deployed in containers.

The multi-mode green internet of things (IoT) provides a communication support for social assets of smart park connecting to power grid for low-carbon operation. Software defined networking (SDN) and network function virtualization (NFV) can flexibly integrate heterogeneous communication modes through network resource scheduling and route management. However, the joint optimization of virtual network functions (VNF) embedding and flow scheduling faces several challenges of differentiated QoS guarantee, coupling and externality of VNF embedding, and route selection conflicts. In this work, a multi-timescale VNF Embedding and floW Scheduling algorithm named NEWS is proposed to maximize throughput while reducing VNF embedding cost and energy consumption. Specifically, the joint optimization problem is transformed into three subproblems, i.e., large-timescale VNF embedding, small-timescale admission control, small-timescale route selection and computation resource allocation. A swap matching-based low-cost VNF embedding algorithm is proposed for the first subproblem. Then, a queue backlog threshold-based admission control strategy is proposed for the second subproblem. Next, the third subproblem is decomposed into two stages, where a collaborative Q-learning-based backpressure-aware algorithm is presented in the first stage, and a greedy-based computation resource allocation algorithm is given in the second stage. Simulations demonstrate that NEWS performs superior in throughput, embedding cost, and energy consumption.