Load Adaptive Caching Points for a Content Distribution Network

The unprecedented growth in content demand on smartphones has significantly increased the energy consumption of current cellular and backbone networks. Apart from achieving stringent carbon footprint targets, provisioning high data rates to city vehicular users while maintaining quality of service (QoS) remains a serious challenge. In previous work, to support content delivery at high data rates, the number and locations of caching points (CPs) within a content distribution network (CDN) were optimized while reducing the operational energy consumption compared to typical cellular networks. Further reduction in energy consumption may be possible through sleep cycles, which reduces transmission energy consumption. However, sleep cycles degrade the quality of service. Therefore, in this paper, we propose a novel load adaptation technique for a CP which not only enhances content download rate but also reduces transmission energy consumption through random sleep cycles. Unlike a non-load adaptive (deterministic) CP, the performance results reveal that the load adaptive CP achieves considerably lower average piece delay (approximately 60% on average during the day), leveraging the introduction of random sleep cycles to save transmission energy. The proposed CP saves up to 84% transmission energy during off-peak hours and 33% during the whole day while fulfilling content demand in a city vehicular environment

Standalone Green Cache Points for Vehicular Content Distribution Networks

With the rapid growth of interest in media-rich user experience, content distribution networks (CDNs) gained considerable attention. Since, most of the energy is consumed by cache points (CPs) and the associated equipment, it is imperative to deploy fewer number of CPs or switch off as many as possible to save energy. This results in degraded quality of service (QoS). It is an usual dimensioning technique to optimise the number and locations of caching points (CPs) of a content distribution network (CDN), where the objective is to reduce operational energy. In this paper, we reduce non-renewable energy consumption (carbon footprint) by introducing renewable grid energy (in the form of wind energy) and adaptive CPs. Further, we propose algorithms for provisioning high number of simultaneous downloads, which reduce overall waiting time and number of dropped request of city vehicular users. The end result is substantial improvement in quality of service (QoS). The proposed CPs save 100% grid energy during the whole day while fulfilling content demand in a city vehicular environment

Green Vehicular Content Distribution Network

With environmental awareness becoming a global concern, content distribution has become popular in the context of modern city scenario with obvious concerns for ICT power consumption. The business world demands huge amounts of information exchange for advertisement and connectivity, which is an integral part of a smart city. In this thesis, a number of energy saving and performance improvement techniques are proposed for the content delivery scenario. These are: content cache location optimisation techniques for energy saving and transceiver load adaptive techniques that save energy while maintaining acceptable piece delay. With the recent advancement in Fog computing, nano-servers are introduced in the later part of the thesis for content delivery and process of user demands. Two techniques random sleep cycles and rate adaptation are proposed to save transmission energy. The quality of service in terms of piece delay and dropping probability are optimised by deploying renewable and non-renewable energy powered nano-servers (NS). Finally, mixed integer linear programming models (MILP) were developed alongside other optimisations methods like bisection, greedy and genetic algorithms which judiciously distribute renewable energy to the fog servers in order to minimise the piece delay and dropping probability in heavily loaded regions of the city area