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 server's 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.

The rapid growth in the energy consumption of mobile networks has become a major concern for mobile operators. Today's mobile networks' usage is dominated by over-the-top (OTT) applications, and operators are keen to determine the network energy consumed by these OTT applications. With a recent shift in user behavior toward a preference for instant messaging (IM) applications over conventional mobile services, operators are interested in exploring what impact OTT IM applications such as WeChat will have on the energy consumption of a network when compared with a corresponding conventional mobile service. Here, we present for the first time energy assessment models for mobile services based on real network and service measurements to address this need. Using WeChat as an OTT IM application example, our results show that WeChat consumes more network energy than conventional mobile services for both light users and heavy text users due to the network signaling energy overhead. In comparison, for heavy voice users, WeChat consumes less network energy since voice messages are first recorded and then sent in packet bursts. Our findings provide a quantitative analysis of the energy consumption of mobile services, which should be valuable for mobile operators and OTT application developers to improve the energy-efficiency of mobile applications and services.

Interactive cloud computing and cloud-based applications are a rapidly growing sector of the expanding digital economy because they provide access to advanced computing and storage services via simple, compact personal devices. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the power consumption of the network and end-user devices when accessing the cloud. In this paper, we develop a power consumption model for interactive cloud applications that includes the power consumption of end-user devices and the influence of the applications on the power consumption of the various network elements along the path between the user and the cloud data centre. As examples, we apply our model to Google Drive and Microsoft Skydrive's word processing, presentation and spreadsheet interactive applications. We demonstrate via extensive packet-level traffic measurements that the volume of traffic generated by a session of the application vastly exceeds the amount of data keyed in by the user. This has important implications on the overall power consumption of the service. We show that using the cloud to perform certain tasks consumes more power (by a watt to 10 watts depending on the scenario) than performing the same tasks locally on a low-power consuming computer and a tablet.

Online social networks (OSNs) with their huge number of active users consume significant amount energy both in the data centers and in the transport network. Existing studies focus mainly on the energy consumption in the data centers and do not take into account the energy consumption during the transport of data between end-users and data centers. To indicate the amount of the neglected energy, this paper provides a comprehensive framework and a set of measurements for understanding the energy consumption of cloud applications such as photo sharing in social networks. A new energy model is developed to estimate the energy consumption of cloud applications and applied to sharing photos on Facebook, as an example. Our results indicate that the energy consumption involved in the network and end-user devices for photo sharing is approximately equal to 60% of the energy consumption of all Facebook data enters. Therefore, achieving an energy-efficient cloud service requires energy efficiency improvement in the transport network and end-user devices along with the related data centers.

Routers and switches are major contributors to the energy consumption of modern networks. Today, many energy efficiency metrics for these high-capacity devices are coarse-grained, i.e., based upon a single energy per bit value given at peak load or averaged over several specific loads. In this paper, we develop a new power model and a vendor-agnostic methodology that permits quantifying the energy efficiency of Internet equipment at a more fundamental level, i.e., at the granularity of per-packet processing, and per-byte store and forward packet handling operations. We demonstrate the efficacy of the proposed technique by applying it to various types of routers and switches. We describe how our technique can be used to accurately estimate the network-wide energy footprint incurred when accessing different applications. We offer our method as a valuable framework against which the energy efficiency of current and future generation of load-proportional Internet equipment can be benchmarked.

Cloud computing and cloud-based services are a rapidly growing sector of the expanding digital economy. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the network transport energy and the additional power consumed by end-user devices when accessing the cloud. In this paper, we develop a simple model to estimate the incremental power consumption involved in using interactive cloud services. We then apply our model to a representative cloud-based word processing application and observe from our measurements that the volume of traffic generated by a session of the application typically exceeds the amount of data keyed in by the user by more than a factor of 1000. This has important implications on the overall power consumption of the service. We provide insights into the reasons behind the observed traffic levels. Finally, we compare our estimates of the power consumption with performing the same task on a low-power consuming computer. Our study reveals that it is not always energy-wise to use the cloud. Performing certain tasks locally can be more energy-efficient than using the cloud.

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.

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).

Computer simulation of a 2.5-Gb/s intensity-modulated/direct-detected optical system with high path dispersion is described. This simulation, based on single-mode laser diode rate equations, includes transmit and receiver circuit filtering, receiver circuit noise, avalanche photodiode noise, fiber dispersion, and laser chirp. The rate equations are sufficiently general to model mode-offset distributed feedback laser diodes. The system power penalty, due to laser chirp and fiber dispersion, is calculated using Gaussian quadrature numerical integration. By simulating a population of some 12800 laser diodes and correlating the performance of each laser in a transmission system with its spectral characteristics, it is possible to deduce laser specifications that will assure satisfactory operation in long-span links. These results are used to study a laser diode specification under consideration by CCITT, and, by studying the simulated laser line shapes, some modifications to the CCITT specification are considered.>