Big Data Meets Telcos: A Proactive Caching Perspective

Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent advances in storage/memory, context-awareness, edge/cloud computing, and falls into framework of big data. However, the big data by itself is yet another complex phenomena to handle and comes with its notorious 4V: velocity, voracity, volume and variety. In this work, we address these issues in optimization of 5G wireless networks via the notion of proactive caching at the base stations. In particular, we investigate the gains of proactive caching in terms of backhaul offloadings and request satisfactions, while tackling the large-amount of available data for content popularity estimation. In order to estimate the content popularity, we first collect users' mobile traffic data from a Turkish telecom operator from several base stations in hours of time interval. Then, an analysis is carried out locally on a big data platform and the gains of proactive caching at the base stations are investigated via numerical simulations. It turns out that several gains are possible depending on the level of available information and storage size. For instance, with 10% of content ratings and 15.4 Gbyte of storage size (87% of total catalog size), proactive caching achieves 100% of request satisfaction and offloads 98% of the backhaul when considering 16 base stations.Comment: 8 pages, 5 figure

Reinforcement learning for proactive content caching in wireless networks

Proactive content caching (PC) at the edge of wireless networks, that is, at the base stations (BSs) and/or user equipments (UEs), is a promising strategy to successfully handle the ever-growing mobile data traffic and to improve the quality-of-service for content delivery over wireless networks. However, factors such as limitations in storage capacity, time-variations in wireless channel conditions as well as in content demand profile pose challenges that need to be addressed in order to realise the benefits of PC at the wireless edge. This thesis aims to develop PC solutions that address these challenges. We consider PC directly at UEs equipped with finite capacity cache memories. This consideration is done within the framework of a dynamic system, where mobile users randomly request contents from a non-stationary content library; new contents are added to the library over time and each content may remain in the library for a random lifetime within which it may be requested. Contents are delivered through wireless channels with time-varying quality, and any time contents are transmitted, a transmission cost associated with the number of bits downloaded and the channel quality of the receiving user(s) at that time is incurred by the system. We formulate each considered problem as a Markov decision process with the objective of minimising the long term expected average cost on the system. We then use reinforcement learning (RL) to solve this highly challenging problem with a prohibitively large state and action spaces. In particular, we employ policy approximation techniques for compact representation of complex policy structures, and policy gradient RL methods to train the system. In a single-user problem setting that we consider, we show the optimality of a threshold-based PC scheme that is adaptive to system dynamics. We use this result to characterise and design a multicast-aware PC scheme, based on deep RL framework, when we consider a multi-user problem setting. We perform extensive numerical simulations of the schemes we propose. Our results show not only significant improvements against the state-of-the-art reactive content delivery approaches, but also near-optimality of the proposed RL solutions based on comparisons with some lower bounds.Open Acces

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends