Power Profiling the Internet Core: A Sensitivity Analysis

Advances in Intelligent Systems and Computing, 2014

Environmental assessments of digital services seeking to take into account the Internet's energy footprint typically require models of the energy intensity of the Internet. Existing models have arrived at conflicting results. This has lead to increased uncertainty and reduced comparability of assessment results. We present a bottom-up model for the energy intensity of the Internet that draws from the current state of knowledge in the field and is specifically directed towards assessments of digital services. We present the numeric results and explain the application of the model in practice. Complementing the previous chapter that presented a generic approach and results for access networks and customer premise equipment, we present a model to assess the energy intensity of the core networks, yielding the result of 0.052kWh/GB.

IEEE Communications Magazine, 2014

With more and more activities taking place online concern over the environmental impact of digital services has drawn attention to the energy intensity of the network.

2014

Assessing the average energy intensity of Internet transmissions is a complex task that has been a controversial subject of discussion. Estimates published over the last decade diverge by up to four orders of magnitude — from 0.0064 kilowatt-hours per gigabyte (kWh/GB) to 136 kWh/GB. This article presents a review of the methodological approaches used so far in such assessments: i) top–down analyses based on estimates of the overall Internet energy consumption and the overall Internet traffic, whereby average energy intensity is calculated by dividing energy by traffic for a given period of time, ii) model-based approaches that model all components needed to sustain an amount of Internet traffic, and iii) bottom–up approaches based on case studies and generalization of the results. Our analysis of the existing studies shows that the large spread of results is mainly caused by two factors: a) the year of reference of the analysis, which has significant influence due to efficiency gains in electronic equipment, and b) whether end devices such as personal computers or servers are included within the system boundary or not. For an overall assessment of the energy needed to perform a specific task involving the Internet, it is necessary to account for the types of end devices needed for the task, while the energy needed for data transmission can be added based on a generic estimate of Internet energy intensity for a given year. Separating the Internet as a data transmission system from the end devices leads to more accurate models and to results that are more informative for decision makers, because end devices and the networking equipment of the Internet usually belong to different spheres of control.

Advances in Intelligent Systems and Computing, 2014

Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet's energy intensity that takes into account different modeling paradigms for different parts of the network.

2011 IEEE 12th International Conference on High Performance Switching and Routing, HPSR 2011, 2011

Advanced power management capabilities have been proposed to be included into next-generation green network devices in order to modulate their energy requirements according to the workload. The clear side effect of enabling these new capabilities consists in a performance level reduction of network devices. Starting from some existing benchmarking standards for evaluating energy-efficiency, namely ECR and ATIS-060015, this contribution is devoted to determining a set of parameters, and methodologies that can be applied to correctly and precisely evaluate the tradeoff between energy consumption and network performance. Some experimental results obtained with the proposed indexes and methodologies, as well as a green SW router prototype have been provided.

IEEE Network, 2000

This article provides an overview of a network-based model of power consumption in Internet infrastructure. This model provides insight into how different parts of the Internet will contribute to network power as Internet access increase over time. The model shows that today the access network dominates the Internet's power consumption and, as access speeds grow, the core network routers will dominate power consumption. The power consumption of data centers and content distribution networks is dominated by the power consumption of data storage for material that is infrequently downloaded and by the transport of the data for material that is frequently downloaded. Based on the model several strategies to improve the energy efficiency of the Internet are presented.

2014 IEEE Online Conference on Green Communications (OnlineGreenComm), 2014

This paper summarizes the energy efficiency improvement obtained by implementing a number of techniques in the core network investigated by the GreenTouch consortium. These techniques include the use of improved components with lower power consumption, mixed line rates (MLR), energy efficient routing, sleep and physical topology optimization. We consider an example continental network topology, NSFNET, to evaluate the total power consumption of a 2010 network and a 2020 network. The 2020 network results are based on traffic projections, the reductions in the equipment power consumption expected by 2020 and a range of energy saving measures considered by GreenTouch as outlined above. The projections of the 2020 equipment power consumption are based on two scenarios: a business as usual (BAU) scenario and a Green Touch (GT) (i.e. BAU+GT) scenario. The results show that the 2020 BAU scenario improves the network energy efficiency by a factor of 4.8x compared to the 2010 network as a result of the reduction in the network equipment power consumption. Considering the 2020 BAU+GT network where the equipment power consumption is reduced by a factor of 27x compared to the 2010 network, and where sleep, MLR and network topology are jointly optimized, a total improvement in energy efficiency of 64x is obtained.

IEICE Transactions on Communications

This paper summarizes recent reports on the internet's energy consumption and the internet's benefits on climate actions. It discusses energy-efficiency and the need for a common standard for evaluating the climate impact of future communication technologies and suggests a model that can be adapted to different internet applications such as streaming, online reading and downloading. The two main approaches today are based on how much data is transmitted or how much time the data is under way. The paper concludes that there is a need for a standardized method to estimate energy consumption and CO 2 emission related to internet services. This standard should include a method for energy-optimizing future networks, where every Wh will be scrutinized.

Los autores inician explicando que es El Cuadrante del Flujo de Dinero y no es nada más que la división que ellos hacen acerca de los tipos de personas que podemos encontrar en cualquier parte del mundo: E = empleados A = auto empleados

Middle East Eye, 2024

From being used as a bulwark against the Soviet left to being told that opposition to Israel's mass murder in Gaza is wrong, the weaponization of Islam for Western political ends goes on.