By Susan White, Head of Strategy and Portfolio Marketing at Netcracker
The composition, complexity and flexibility of 5G networks create huge challenges for communications service providers (CSPs) looking to exploit the full potential of the technology. Although 5G’s network slicing capability will open up a massive new range of use cases to be explored and monetised, the sheer scale of the opportunity has yet to create a common approach to harnessing it.
Capitalising on these new revenue streams relies on the efficient and effective management of the 5G network behaviours. Already a complex process, this will become even more complicated with 5G network slicing, with the myriad slice attributes, relationships between elements, interdependencies and mappings.
Slicing introduces arguably infinite configurations in terms of creating, introducing, managing and enforcing network policies, and it is simply not possible to implement and execute these in significant volumes without intent-based automation.
Intent-based automation is the game-changer required to truly release and exploit the potential of 5G. It brings together design tools, orchestration, real-time inventory, artificial intelligence and machine learning to automate services and resources across the network – replacing complex, deterministic workflow automation and often manual processes that required a great deal of technical expertise.
The automation that flows from the service intent ideally needs to be total. What makes this harder with 5G and slicing is enabling the network to dynamically adapt to the changing service or business needs. In other words, automation needs to be intelligent. With cloud-native network functions, ever more frequent software releases and upgrades, private and public cloud platforms and the associated increase in the number of participants across the ecosystem, the automation has to be service-driven across the entire network. Mixing in manual processes needs to be minimised as much as possible and eventually eliminated.
The ‘intent’ in intent-based automation means a business or service request – examples are maybe opening a new smart office or updating a security policy across all operations. The request needs to be fulfilled irrespective of the technical platform – administrators should be able to simply express their intent, and the automated systems carry out the task without human intervention.
It goes without saying that automating operations such as the examples above would not only save time and cost but should also give CSPs significant competitive advantages in terms of exceeding customer expectations, delivering change, improvements and new services faster and more efficiently.
Note the ‘would’ and the ‘should’ here – because enabling intent-based automation is far from a straightforward process. The quality of the automation is only as good as the service (or slice) model and how it’s implemented and executed.
Before intent can be implemented, a library of predefined slice service models for popular use cases must first be created in order to define the intent rules, policies and critical parameters (eg SLAs and end points). The models need to be designed to ensure the SLAs in place for each network slice are maintained – which means dynamically adapting to the various requirements of the slice services. However, slices span multiple layers of a network and no single model can define all of the required operational lifecycle transitions, control-loop logic, slice-service relationships and mappings, and topology and capacity dependencies of a network slice. To avoid the complexity of building multiple models, a composite data model is needed in which parts of a data model are inserted into another.
It’s now the orchestration’s job to interpret and implement the slice service model. This important step requires a structured knowledge graph with weighted associations that proceeds with soft decision making related to which transitions to make to achieve the target state. The transitions can include a modification, a scale-out or a feasibility check (or all three) and the orchestration system needs to generate a sequence of automation steps without having to do extensive modelling for every possible state transition. During implementation, the orchestration system relies on exposed capabilities from southbound systems to implement the intent.
The final step is how the intent is executed in the orchestration system. With a highly dynamic network, the traditional centralised workflow approach is not efficient and in fact, becomes a bottleneck. A new distributed choreography approach is needed that dynamically composes services in a decentralised way to achieve greater scale and efficiency.
Achieving intent-based automation is the goal for CSPs who want to attain the level of control that is imperative for the delivery of complex services and the end-to-end management of network slicing. Together with dynamic service models and a new implementation and execution approach, this intelligent automation will simplify operations, creating more agile and flexible networks that can do justice to the promises of 5G.
