30 January 2018

A.I. for mitigating the "complexity" of the Digital Transformation

Several techno-economic drivers which are paving the way to a profound digital transformation of the Telecommunications infrastructures. Among these drivers, there are: the diffusion of ultra-broadband, the increasing of performance of IT systems vs the down-spiralling costs, emerging of innovative networks and services paradigms such as SDN and NFV, the growing availability of open source software but also the impressive advances of Machine Learning and Artificial Intelligence.

This digital transformation will lead the current legacy Telecommunications infrastructures to evolve towards the 5G (the 5th generation of network infrastructures) as an end-to-end network and service platform: in the long term, 5G is set to integrate processing, memory/storage and networking resources, functions and services through a “transparent”, hyper-connected ultra-broadband programmable “fabric”.

In this direction, in several Standardization Bodies and Fora, an high-level reference model is emerging, based on two main pillars: 1) an infrastructure physical layer, which will include computing, memory/storage and network resources (up to the edge/fog resources and even the Users’ terminals, devices, smart things); 2) a software virtualization layer which will allow providing high-level abstractions of all the infrastructure resources, functions and services.

It is well known that Software-Defined Networks (SDN) and Network Function Virtualization (NFV) are two of the key enabling technologies. Their exploitation in 5G will allow Virtualized Network Function (VNF) and services will be dynamically combined and orchestrated to create specific end-to-end “service chains” for the vertical applications; moreover the infrastructure will provide “slices” of logical resources where to execute multiple chains to serve applications (specific QoS requirements).

It is also reasonable to expect that this network transformation will reduce the costs (e.g., CAPEX and OPEX) and increase the flexibility of the infrastructure, ensuring high levels of programmability (through APIs) and the performance and security levels required by future 5G scenarios and applications (e.g., Internet of Things, Tactile Internet, Immersive Communications, Automotive, Indutry4.0, Smart Agriculture, Genomics/Omics and E-Health, etc).

So 5G will be much more than one step beyond today’s 4G-LTE networks: it is expected to become a an end-to-end network and service platform where multi-level APIs will allow Operators/Providers, Third Parties or even end-Users to create/operate “service chains”, made of elementary services/functions component capable of meeting on-demand the applications’ requirements. As a matter of fact, 5G architectural and functional disaggregation is one of the most debated avenues in innovation and standardization activities. 

We are witnessing a rapidly increasing in the “complexity” of the infrastructures subjected to this process of digital transformation, a complexity which will be too high just for human-made operators.

In fact, configuration, control and management of current physical pieces of equipment (in most cases closed boxes) will have to be replaced by automated processes acting over millions of virtual/logical entities (e.g., Virtual Machines, Containers, appliances etc). Management (e.g., Fault, Configuration, Accounting, Performance and Security) control and orchestration functions of such future infrastructures will require innovative methods and systems (e.g., self-organizing, adaptive control, machine learning, neural networks, etc.) capable of using the big data to mitigate this “complexity”.

It is not only a technical “complexity” but also an economic one, about biz sustainability. The increasing competition pressure in the Telecommunications market is pushing Network Operators and Service Providers to look for new services scenarios and solutions for reducing/optimising the overall operations costs to compensate the cases where revenues are declining.    

It is expected that A.I. (e.g., ML over actionable Big Data, etc...) will help for mitigating the "complexity" of this Digital Transformation, but what will be its impact on the networks and services platforms architectures ?

It's not just a matter of mathematical methods or algorithms, heuristics, etc.. What A.I. functions, what interfaces to what have to be standardized ?