Intelligence-Endogenous Management Platform for Computing and Network Convergence

TL;DR

This paper introduces the CNC brain, an AI-driven management platform for computing and network convergence, designed to automatically optimize resource allocation and task scheduling in heterogeneous CNC environments.

Contribution

It proposes the concept and framework of an intelligent management platform for CNC, and demonstrates its effectiveness through a prototype using deep reinforcement learning.

Findings

Improved resource utilization and performance in CNC testbed

Effective automatic matching of supply and demand in heterogeneous environments

Prototype validated with real-world dataset and open-source frameworks

Abstract

Massive emerging applications are driving demand for the ubiquitous deployment of computing power today. This trend not only spurs the recent popularity of the \emph{Computing and Network Convergence} (CNC), but also introduces an urgent need for the intelligentization of a management platform to coordinate changing resources and tasks in the CNC. Therefore, in this article, we present the concept of an intelligence-endogenous management platform for CNCs called \emph{CNC brain} based on artificial intelligence technologies. It aims at efficiently and automatically matching the supply and demand with high heterogeneity in a CNC via four key building blocks, i.e., perception, scheduling, adaptation, and governance, throughout the CNC's life cycle. Their functionalities, goals, and challenges are presented. To examine the effectiveness of the proposed concept and framework, we also implement a prototype for the CNC brain based on a deep reinforcement learning technology. Also, it is evaluated on a CNC testbed that integrates two open-source and popular frameworks (OpenFaas and Kubernetes) and a real-world business dataset provided by Microsoft Azure. The evaluation results prove the proposed method's effectiveness in terms of resource utilization and performance. Finally, we highlight the future research directions of the CNC brain.