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

Energy Efficient Nano Servers Provisioning for Information Piece Delivery in a Vehicular Environment

In this paper, we propose energy efficient Information Piece Delivery (IPD) through Nano Servers (NSs) in a vehicular network. Information pieces may contain any data that needs to be communicated to a vehicle. The available power (renewable or non-renewable) for a NS is variable. As a result, the service rate of a NS varies linearly with the available energy within a given range. Our proposed system therefore exhibits energy aware rate adaptation (RA), which uses variable transmission energy. We have also developed another transmission energy saving method for comparison, where sleep cycles (SC) are employed. Both methods are compared against an acceptable download time. To reduce the operational energy, we first optimise the locations of the NSs by developing a mixed integer linear programming (MILP) model, which takes into account the hourly variation of the traffic. The model is validated through a Genetic Algorithm (GA1). Furthermore, to reduce the gross delay over the entire vehicular network, the available renewable energy (wind farm) is optimally allocated to each NS according to piece demand. This, in turn, also reduces the network carbon footprint. A Genetic Algorithm (GA2) is also developed to validate the MILP results associated with this system. Through transmission energy savings, RA and SC further reduce the NSs energy consumption by 19% and 18% respectively, however at the expense of higher download time. MILP model 4 (with RA) and model 5 (with SC) reduced the delay by 81% and 83% respectively, while minimising the carbon footprint by 96% and 98% respectively, compared to the initial MILP model

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

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