Coding-theoretic approaches to distributed caching, storage and computing

Abstract

The next-generation communication networks aim to bring data storage and computation closer to the end users and Internet of things (IoT) endpoints by exploiting edge servers and/or memory available at edge devices. This greatly improves the efficiency and economics of network applications. Traditionally, coding techniques have been applied to communication problems with terrific gains in performance. In this thesis, we apply novel coding techniques to edge storage and computation problems, and show similar significant gains in terms of storage costs, communication costs, computation costs, and privacy.

In the first part of the thesis, we study a wireless distributed network of small base stations (SBS) which serve cache-enabled users. We propose a novel scheme of coded caching in a multi-server system with a random connectivity pattern, and study the fundamental limits of the trade-off between the storage capacity at the servers, the cache capacity at the users, and the delivery latency.

We then study the trade-off between the storage capacity and bandwidth requirement for the repair of failed storage nodes/SBSs in a distributed wireless content caching system. We derive the fundamental limits of this trade-off using techniques from information theory, and propose a storage and repair framework to achieve the optimal trade-off, while satisfying the practical constraints of low subpacketization, low overheads, and low computational complexity.

We then address problems related to distributed edge computing systems. In particular, we address computational privacy problems, where a user offloads the computation of a function on a set of matrices to powerful distributed servers, while preserving the privacy of the data and the computation result from T colluding servers as well as the user, and also achieving significantly smaller communication costs than existing secure multiparty computation techniques.