Communication networks have emerged to become the basic infrastructure for all areas of our society with application areas ranging from social media to industrial production and healthcare. New requirements include the need for dynamic changes of required resources, for example, to react to social events, to shifts of demands or to adoption of new requirements. Existing networks and, in particular, the Internet cannot meet those requirements mainly due to their ossification, and hence limitation in resource allocation, i.e. lack of flexibility to adapt the available resources to changes of demands on a small time-scale and in an efficient way.
In recent years, several concepts have emerged in networking research to provide more flexibility in networks through virtualization and control plane programmability, summarized with the term network softwarization: Software-Defined Networking (SDN), Network Virtualization (NV) and Network Function Virtualization (NFV).
However, a deeper understanding of what flexibility means remains open. In this project, flexibility focuses on the dynamic changes of a network that is characterized by its resources (link rate and node capacities), connectivity (network graph) and its network functions with their related resources, (processing and storage) and their deployment locations. It is the objective of this research to analyze the fundamental design space for flexibility in softwarized networks with respect to cost such as resource usage, performance impact, e.g. latency, and adaptation overhead, e.g. migration. The outcome is the definition of a measure for network flexibility. An analytical model for the definition of such flexibility measure to quantitatively compare different network design choices and to assess the trade-off for flexibility vs. cost has been developed. The design space analysis includes mechanisms for network softwarization as well as general network characteristics such as graph properties and technologies for system optimization based on machine learning techniques. The detailed analysis is based on use cases from different areas including: dynamic resource allocation, function placement, softwarized wireless networks, and resilience.
A fundamental understanding of network flexibility manifested in a quantitative measure and related design guidelines enables all stakeholders in networking to come up with a future-proof system design addressing the dynamic and unforeseeable changes. Thus, it provides a significant benefit to our society as a whole that heavily relies on communication networks. In particular, operators are empowered to react to the emergence of new technologies and regulatory changes. Network flexibility is a key decision factor between network designs, and envisaged as a tie-breaking decisive advantage for a certain network design or technology. For implementation and operation decisions taken by the communications industry, it fills a gap to better understand the options provided by network softwarization and beyond. For research and development, a fundamental understanding of network flexibility supports the analysis of which technical concepts lead to more flexibility in network design to enable generic guidelines for more flexible systems.
