From the diagram, it is clear that at the highest levels, the network slices can dynamically come and go, at the middle layer, the VNFs can come, go or resize to support the traffic, while at the bottom layer, physical nodes and links can be added or fail; with dynamic interactions among every level. A slice can have multiple alternate realization possibilities and during its life, the particular realization may have to change to another due to a variety of reasons, such as the come-up of another slice or the failure of a VNF or a physical node or link. To make the matter even more complex, it is expected that 5G deployments will include many small regional operators that provide the shared access network for the larger operators or enterprises operating their own 5G networks that might connect with bigger 5G networks for roaming or utilize the services of carrier-scale networks for failover and interconnectivity. For various business reasons, there is also industry momentum to move toward disaggregated nodes to allow for mixing and matching of network components.
In the above dynamic environment, we critically need a mapping technique that can efficiently model the current state of the network and the relationships of a slice to VNF/software infrastructure/physical nodes. For the solution to scale, we need it to work across various domains and be dynamic and resilient in the event of a node failure or traffic surge. Further, it should be possible for a repository to add new nodes automatically and update the relationships to include new nodes of potentially different types. The repository should allow one to search based on desired properties or discover new relationships. Each node should be describable by its model and the repository must allow easy extensions to include new models across various operating domains.
Traditionally, relational databases or tree-based schemas have been used to keep track of the relationships of services and devices in networks. The disadvantages of such schemas are that they cannot map and model the hierarchical relationship dynamically and cannot be used efficiently to explore indirect relationships that may exist between physical nodes and slices. Therefore, we need a new way to model the state of the network and its component relationships, within a layer or across layers.
Fortunately, there have been recent promising parallel developments in new schemas such as knowledge graphs in order to model, learn about, and investigate complex multi-relational settings. A knowledge graph describes objects of interest and connections between them while also providing a shared view of the knowledge within an organization. They are able to reuse definitions and descriptions that others create. Knowledge graphs are being used by companies such as Google, Facebook and LinkedIn to model and explore the complex relationships that could exist among words of a search query or entities in a social networkii. With physical networks reaching a similar level of dynamic behavior, complexity, and multi-layer-relationships, we should effectively evaluate the use of knowledge-graph based schemas to model the relationship between services, slices, VNFs and network nodes. These knowledge graphs should also take advantage of existing network semantics.
Having a knowledge graph-based repository will help us in modeling and exploring inter-layer and intra-layer relationships and discover new facts in network operations. These facts, available via simple queries or through automatic discovery, will assist human operators with root-cause analysis and planning, and will also help develop intelligent network applications and features to improve network efficiency. New networks are getting increasingly complex to be run by human operators and it is important to think of a unifying node and state repository that can match the complexity and dynamisms of today’s and tomorrow’s network.
[i] Next Generation Protocols (NGP) E2E Network Slicing Reference Framework and Information Model , ETSI GR NGP 011 v1.1.1 (2018-09) https://www.etsi.org/deliver/etsi_gr/NGP/001_099/011/01.01.01_60/gr_NGP011v010101p.pdf
[ii] Industry-Scale Knowledge Graphs: Lessons and Challenges By Natasha Noy, Yuqing Gao, Anshu Jain, Anant Narayanan, Alan Patterson, Jamie Taylor Communications of the ACM, August 2019, Vol. 62 No. 8, Pages 36-43