The edge cloud: A holistic view of communication, computation and caching

TL;DR

This paper advocates for a holistic approach to communication, computation, and caching at the network edge, emphasizing signal processing and graph-based learning to optimize resource allocation for low-latency, high-reliability services.

Contribution

It introduces a unified framework integrating 3C resources at the network edge, highlighting the role of signal processing and graph-based learning for resource optimization.

Findings

Joint optimization of 3C resources improves network performance.

Graph-based learning techniques enable effective resource allocation.

Edge-centric 3C management supports low-latency, reliable services.

Abstract

The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) communication networks. This ambitious goal requires a paradigm shift towards a vision that looks at communication, computation and caching (3C) resources as three components of a single holistic system. The further step is to bring these 3C resources closer to the mobile user, at the edge of the network, to enable very low latency and high reliability services. The scope of this chapter is to show that signal processing techniques can play a key role in this new vision. In particular, we motivate the joint optimization of 3C resources. Then we show how graph-based representations can play a key role in building effective learning methods and devising innovative resource allocation techniques.