Dynamic and Scalable Control as a Foundation for Future Networks

The authors see problems with current network control models. Their control networks (i.e., control channels, necessary for control operation) are not thought of as part of the control model itself. Current network control is not transactional. Network updates are neither atomic nor isolated, and the application is not aware of the details of an update outcome. This chapter presents an alternative design in which the control channel is an integral part of the network control model. Its key part is a robust, in-band resource connectivity layer that interconnects all available network elements, including the controller(s). The control is also transactional. Applications can safely assume that their updates will not clash in the network, as well as that they will always affect the right, intended fraction of the network. Building on these two postulates, the authors see service scheduling as its third essential part of network control. The scheduling takes service requirements into account and assigns the services network resources that will meet their requirements.

Model-Centric Fulfillment Operations and Maintenance Automation

As networks have evolved, there has been an evolution in how they are managed as well. This evolution has seen a move from manual configuration via command line interface (CLI) to script-based automation and eventually to a template-based approach with workflow to coordinate multiple templates and scripts. The next step in this evolution is the introduction of models to provide a more dynamic capability than is in place today. This chapter will discuss three major layers of modelling that should be considered during implementation of this approach: device models focused on the configuration of the hardware itself; service models focused on the customer or network facing services that leverage the hardware level configuration; and operational models focused on people, processes, and tools involved in application of device and service models. This includes the orchestration of activities with other tools, such as operational support systems (OSS) and business support systems (BSS).

Slicing Challenges for Operators

The advent of 5G introduces the concept of network slicing which is meant to permit network service providers to overcome the great challenge of forthcoming 5G services: how to support and operate different kinds of services with very distinct needs onto the same infrastructure. Deploying altogether on the same network makes it quite difficult to define a common architecture capable of keeping the diverse requirements of all of them. The network slicing concept foresees a number of logically independent slices, each comprising different network nodes and service functions, which are interconnected and are involved in the delivery and the operation of a specific service. By instantiating network slices, the network will be able to provide completely different services in a dynamic way over the same infrastructure. This chapter overviews the challenges raised by the implementation of the network slicing concept and which will be faced by the network operators.

Future Networks

This chapter gives an insight into some challenging combinatorial optimization problems that have to be tackled to deliver efficient and appropriate decision algorithms to manage future networks. The first part of the chapter is dedicated to variants of routing optimization problems in future IP networks, and the second part is dedicated to two optimization problems related to network virtualization and 5G network slicing, the virtual network embedding problem and the service function chaining problem. Each of these optimization problems is described along with the main challenges to overcome, and a recent and extensive related state of the art is given, so as to highlight the most recent and promising approaches to solve them.

Autonomic Networking Integrated Model and Approach (ANIMA)

This chapter presents the work of the Autonomic Networking Integrated Model and Approach (ANIMA) working group of the Internet Engineering Task Force (IETF). It was formed to standardize protocols and procedures for an ANIMA autonomic network (AN) and first chartered to define the ANIMA secure autonomic network infrastructure (ANI). This chapter describes the technical history and goals leading to this working group. It then describes how the ANIMA approach provides an evolutionary approach to securing and automating networks and to provide a common infrastructure to evolve into future autonomic networks. Finally, this chapter compares this approach to adjacent standards technologies and discusses interesting next steps.

A Unifying Framework Design for the Management of Autonomic Network Functions

The increased use of software-driven and virtualization techniques enables more versatile network infrastructures. Realizing the full potential of such large and dynamic systems requires advanced automation and adaptation capabilities. In this chapter, the authors review recent development of so-called self-driving networks combining cognitive techniques and autonomic behaviors. In particular, the authors provide insights on a set of core mechanisms for the operation of self-driving networks: (1) a governance function to help operators deploy, pilot, control, and track run-time behaviors and performance of self-driving functions; (2) a coordination function to ensure stability and performance when several self-driving functions are running together; (3) a knowledge function to share relevant information to empowering their actions; and (4) common workflows, lifecycles, and APIs to enable deployment and interoperability of autonomic functions. The analysis connects with reference work in scientific literature and the most recent developments in standards (e.g., IETF/IRTF and ETSI).

Self-Driving Networks

This chapter presents a new vision of network operations, the self-driving network, that takes automation to the next level. This is not a description of existing work; rather, it is a challenge to dramatically rethink how we manage networks (or rather, how we do not manage networks). It draws upon an analogy with the development of self-driving cars and presents motivations for this effort. It then describes the technologies needed to implement this and an overall architecture of the system. As this endeavor will cause a major shift in network management, the chapter offers an evolutionary path to the end goal. Some of the consequences and human impacts of such a system are touched upon. The chapter concludes with some research topics and a final message. Key takeaways are that machine learning and feedback loops are fundamental to the solution; a key outcome is to build systems that are adaptive and predictive, for the benefit of users.

The Open Source Community Choice

Open source communities have had and continue to have a major influence on the evolution of the Internet. By their nature, such communities involve people with diverse coding cultures and skills. Automation has consequently been of major interest to open source software developers for a long time, and many open source tools have been developed to address code variability and sustainability challenges. This chapter discusses why open source communities must automate and the challenges they will face. Solutions and current examples of automation in open source projects are provided as a guide to what is achievable. OpenShift, OpenStack, and OPNFV communities are used to illustrate different approaches and best practices. Two recently initiated automation initiatives are detailed: “Cross Community Continuous Integration” (XCI) and “Cross Testing” (Xtesting). Finally, some recommendations are provided for new projects as a guide to ease adoption of appropriate tools and methods.

Automatic Address Scheduling and Management for Broadband IP Networks

The increase in number, diversity, and complexity of modern network devices and services creates unprecedented challenges for the currently prevailing approach of manual IP address management. Manually maintaining IP addresses could always be sub-optimal for IP resource utilization. Besides, it requires heavy human effort from network operators. To achieve high utilization and flexible scheduling of IP network addresses, it is necessary to automate the address scheduling process in the Internet of the future. Based on analysis of the gap between existing address management methods and emerging requirements of the IP network, this chapter illustrates CASM, a new approach for IP address scheduling, including its background, use cases, requirements, general framework, system architecture, interface, and workflow. A prototype system is developed and evaluated based on data from real-world networks and users in two Chinese provinces. Experimental results demonstrate that our system can largely improve the address utilization efficiency and reduce the workload of network resource maintenance.

Network Security Policy Automation

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.