Methodologies for assessing the use-phase power consumption and greenhouse gas emissions of telecommunications network services.

Internet traffic has grown rapidly in recent years and is expected to continue to expand significantly over the next decade. Consequently, the resulting greenhouse gas (GHG) emissions of telecommunications service-supporting infrastructures have become an important issue. In this study, we develop a set of models for assessing the use-phase power consumption and carbon dioxide emissions of telecom network services to help telecom providers gain a better understanding of the GHG emissions associated with the energy required for their networks and services. Due to the fact that measuring the power consumption and traffic in a telecom network is a challenging task, these models utilize different granularities of available network information. As the granularity of the network measurement information decreases, the corresponding models have the potential to produce larger estimation errors. Therefore, we examine the accuracy of these models under various network scenarios using two approaches: (i) a sensitivity analysis through simulations and (ii) a case study of a deployed network. Both approaches show that the accuracy of the models depends on the network size, the total amount of network service traffic (i.e., for the service under assessment), and the number of network nodes used to process the service.

Energy efficiency of on-demand video caching systems and user behavior.

Energy-efficient video distribution systems have become an important tool to deal with the rapid growth in Internet video traffic and to maintain the environmental sustainability of the Internet. Due to the limitations in terms of energy-efficiency of the conventional server centric method for delivering video services to the end users, storing video contents closer to the end users could potentially achieve significant improvements in energy-efficiency. Because of dissimilarities in user behavior and limited cache sizes, caching systems should be designed according to the behavior of user communities. In this paper, several energy consumption models are presented to evaluate the energy savings of single-level caching and multi-level caching systems that support varying levels of similarity in user behavior. The results show that single level caching systems can achieve high energy savings for communities with high similarity in user behavior. In contrast, when user behavior is dissimilar, multi-level caching systems should be used to increase the energy efficiency.