Abstract. Power management strategies that allow network infrastructures to achieve advanced functionalities with limited energy budget are expected to induce significant cost savings and positive effects on the environment, reducing Green House Gases (GHG) emissions. Power consumption can be drastically reduced on individual network elements by temporarily switching off or downclocking unloaded interfaces and line cards. At the state-of-the-art, Adaptive Link Rate (ALR) and Low Power Idle (LPI) are the most effective local-level techniques for lowering power demands during low utilization periods. In this paper, by modeling and analyzing in detail the aforementioned local strategies, we point out that the energy consumption does not depend on the data being transmitted but only depends on the interface link rate, and hence is throughput-independent. In particular, faster interfaces require lower energy per bit than slower interfaces, although, with ALR, slower interfaces require less energy per throughput than faster interfaces. We also note that for current technologies the energy/bit is the same both at 1 Gbps and 10 Gbps, meaning that the increase in the link rate has not been compensated at the same pace by a decrease in the energy consumption.
This paper analyses the relationships between different service level agreement (SLA) components, that is, the different types of quality metrics employed by infrastructure and service providers. We propose that the quality of network economics (QoNE) be used to evaluate the quality of a network path for a given SLA model. Our evaluation indicates the usefulness of the QoNE metric, and it provides a new mechanism for the networking community to address network design problems when incorporating new challenges, such as the Internet of Things. We show how the proposed model can be implemented in software-defined network architecture applicable to networking technologies that include the interconnection of autonomous systems.Peer ReviewedPostprint (published version
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.