Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure

TL;DR

This paper proposes an architectural framework for delivering AI as a service on software-defined infrastructures, integrating monitoring, analysis, and machine learning pipelines to enable smart applications across various sectors.

Contribution

It introduces a novel SDI-based architecture with a dedicated training and AI-aaS plane, including a sandbox for managing multiple MKL loops and demonstrates its effectiveness through experimental deployments.

Findings

Successful deployment of AI-aaS applications on the SAVI testbed.

Effective data compression and resource allocation using AI techniques.

Enhanced smart transportation classification accuracy.

Abstract

This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops. We present experimental measurement results for three AI-aaS applications deployed on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders; 2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway segment classification in smart transportation.