Abstract
To reap its full benefits, 5G must evolve into a scalable decentralized architecture by exploiting intelligence ubiquitously and securely across different technologies, network layers, and segments. In this article, we propose end-to-end and ubiquitous secure machine learning (ML)-powered intent-based networking (IBN). The IBN framework is aware of its state and context to autonomously take proactive actions for service assurance. It is integrated in a zero-touch control and orchestration framework featuring an ML function orchestrator to manage ML pipelines. The objective is to create an elastic and dynamic infrastructure supporting per-domain and end-to-end network and services operation. The solution is supported by a radio access network and forwarding plane, and a cloud/edge virtualization infrastructure with ML acceleration. The resulting framework supports application-level resilience and intelligence through replication and elasticity. An illustrative intelligent application use case is presented.
Highlights
The new generation of Real Time (RT) mission-critical applications require high resilience and low latency coordinated actions
Note that Machine Learning (ML) models can be subject to attacks, e.g.: i) injecting malicious data to produce ML model bias when used for training; ii) tampering telemetry data to alter ML model inference; or iii) embedding backdoors in the ML models [8]
We propose a secure smart e2e platform targeting network and computing self-optimization to provide committed QoE to intelligent applications
Summary
The new generation of Real Time (RT) mission-critical applications require high resilience and low latency coordinated actions. At every domain (i.e., RAN, transport, and computing from the edge to the metro/core), a technology-dependent orchestrator provides an abstracted view of the domain resources and coordinates a set of underlying Software Defined Networking (SDN) controllers and Virtual Infrastructure Managers (VIM) in charge of data plane programmability. Quality of Service (QoS) telemetry needs to be e2e, from terminals to the cloud, with high accuracy and sub-ms granularity In this context, telemetry data feeds ML algorithms for training, inference, and rapid detection of anomalies and performance degradations, which makes the data plane highly predictable and reliable and includes embedded security to create a distributed barrier to mitigate distributed attacks. The sections tackle the key components of the proposed solution
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