Building more performant large scale networks for the Internet of Things

Abstract

Inspired by the rise of smaller computing devices and ubiquitous network connec- tivity, this dissertation focuses on the dynamics of extremely large scale computer networks and mechanisms to improve their performance. Traditional networks were not designed to support the billions (and possibly more than a trillion) of de- vices that are expected to be part of the Internet of Things (IoT) and eventually the Internet of Everything (IoE). We look at mechanisms to efficiently allocate resources in large networks and show how they can scale with the network size. We tackle the hard problem of power in energy limited networks and show how setting priorities can improve latencies even in huge networks. We devise ways to enhance the mobile computing experience through collaboration and moving the computation away to the network edge. Finally we devise a mechanism to improve caching at the network edge and show that improved caching at the edge can support a vast number of users without sacrificing on the Quality of Service. We propose to extend this work by incorporating mobility for the vast number of smaller devices connecting to the network. Mobility modeling on extremely large scales in computationally very expensive. We propose to use heuristics and machine learning models to improve computation times. Current networks take heavy advantage of the centralized nature of the cloud to compute network traffic flows and take routing decisions. As networks scale, this centralized approach would become infeasible. We propose to take a hybrid approach wherein the cloud only acts as an exchange for these decisions and network management is performed by devices in the network itself. Furthermore, since decisions need not be coordinated centrally, we propose to show that this approach can lead to optimum network performance on a massive scale.