Robert Ayre

Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport

Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circumstances cloud computing can consume more energy than conventional computing where each user performs all computing on their own personal computer (PC).

Energy Consumption in Optical IP Networks

As community concerns about global energy consumption grow, the power consumption of the Internet is becoming an issue of increasing importance. In this paper, we present a network-based model of power consumption in optical IP networks and use this model to estimate the energy consumption of the Internet. The model includes the core, metro and edge, access and video distribution networks, and takes into account energy consumption in switching and transmission equipment. We include a number of access technologies, including digital subscriber line with ADSL2+, fiber to the home using passive optical networks, fiber to the node combined with very high-speed digital subscriber line and point-to-point optical systems. In addition to estimating the power consumption of todays Internet, we make predictions of power consumption in a future higher capacity Internet using estimates of improvements in efficiency in coming generations of network equipment. We estimate that the Internet currently consumes about 0.4% of electricity consumption in broadband-enabled countries. While the energy efficiency of network equipment will improve, and savings can be made by employing optical bypass and multicast, the power consumption of the Internet could approach 1% of electricity consumption as access rates increase. The energy consumption per bit of data on the Internet is around 75\bm muJ at low access rates and decreases to around 2-4 \bm muJ at an access rate of 100 Mb/s.

Evolution of WDM Optical IP Networks: A Cost and Energy Perspective

We review technologies and architectures for WDM optical IP networks from the viewpoint of capital expenditure and network energy consumption. We show how requirements of low cost and low energy consumption can influence the choice of switching technologies as well as the overall network architecture.

Energy Consumption in Access Networks

We present a comparison of energy consumption of access networks. We consider passive optical networks, fiber to the node, point-to-point optical systems and WiMAX. Optical access technologies provide the most energy-efficient solutions.

Power consumption and energy efficiency in the internet

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 Internets 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.

Energy consumption in wired and wireless access networks

Energy consumption is becoming an increasingly important issue throughout the community. For network operators in particular it is a concern as networks expand to deliver increasing traffic levels to increasing numbers of customers. The majority of the energy used by the Internet today is consumed in the access network, and this will continue to be the case for the short-to-mid- term future. Access technologies should thus be a prime focus for energy use mitigation. In this article, we present a detailed analysis of energy consumption in current and future access networks. We present the energy consumption of DSL, HFC networks, passive optical networks, fiber to the node, point-to-point optical systems, UMTS (W-CDMA), and WiMAX. Optical access networks are the most energy efficient of the available access technologies.

Photonic Switching and the Energy Bottleneck

The energy consumption of the Internet is growing exponentially. We examine the potential of photonic switching to reduce energy consumption by determining the contribution of cross connects and buffers to the total energy consumption of the Internet.

Energy consumption in IP networks

A model of energy consumption in IP networks is used to estimate the energy consumption of the public Internet. Today’s Internet uses less than 1% of the available electricity supply. However, network energy consumption could grow substantially as access rates increase.

Fog Computing May Help to Save Energy in Cloud Computing

Tiny computers located in end-user premises are becoming popular as local servers for Internet of Things (IoT) and Fog computing services. These highly distributed servers that can host and distribute content and applications in a peer-to-peer (P2P) fashion are known as nano data centers (nDCs). Despite the growing popularity of nano servers, their energy consumption is not well-investigated. To study energy consumption of nDCs, we propose and use flow-based and time-based energy consumption models for shared and unshared network equipment, respectively. To apply and validate these models, a set of measurements and experiments are performed to compare energy consumption of a service provided by nDCs and centralized data centers (DCs). A number of findings emerge from our study, including the factors in the system design that allow nDCs to consume less energy than its centralized counterpart. These include the type of access network attached to nano servers and nano servers time utilization (the ratio of the idle time to active time). Additionally, the type of applications running on nDCs and factors such as number of downloads, number of updates, and amount of preloaded copies of data influence the energy cost. Our results reveal that number of hops between a user and content has little impact on the total energy consumption compared to the above-mentioned factors. We show that nano servers in Fog computing can complement centralized DCs to serve certain applications, mostly IoT applications for which the source of data is in end-user premises, and lead to energy saving if the applications (or a part of them) are off-loadable from centralized DCs and run on nDCs.

Energy Challenges in Current and Future Optical Transmission Networks

In this paper, we examine how energy constraints might shape future optical communication networks and the impact that current technology trends may have on future energy use. Historical factors and prevailing complications associated with fiber capacity point to an increased focus on energy to enable tighter photonic and electronic component integration and larger networks. Energy requirements and associated challenges are described at the component, system, and network level.