Online Caching with no Regret: Optimistic Learning via Recommendations

The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem through the lens of optimistic online learning. We build upon the Follow-the...

Selective Edge Computing for Mobile Analytics

An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitting and processing large data volumes at distant cloud-deployed servers. However, due to memory and computing limitations, the devices often cannot support the...

Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task is challenging since optimizing the vBS scheduling procedure requires knowledge of parameters, which are erratic and demanding to obtain in advance. In this paper, we...

Performance of complex networks: Efficiency and Robustness

Network performance is determined by the interplay of underlying structures and overlying dynamic processes on networks. This thesis mainly considers two types of collective dynamics on networks, spread and transport, which are ubiquitous in our daily lives, ranging from information propagation, disease spreading, to molecular motors on...