Energy efficiency is increasingly important for future information and communication technologies (ICT), because the increased usage of ICT, together with increasing energy costs and the need to reduce green house gas emissions call for energy-efficient technologies that decrease the overall energy consumption of computation, storage and communications. Cloud computing has recently received considerable attention, as a promising approach for delivering ICT services by improving the utilization of data centre resources. In principle, cloud computing can be an inherently energy-efficient technology for ICT provided that its potential for significant energy savings that have so far focused on hardware aspects, can be fully explored with respect to system operation and networking aspects. Thus this paper, in the context of cloud computing, reviews the usage of methods and technologies currently used for energy-efficient operation of computer hardware and network infrastructure.After surveying some of the current best practice and relevant literature in this area, this paper identifies some of the remaining key research challenges that arise when such energy-saving techniques are extended for use in cloud computing environments.

In this paper, a new architecture for sharing resources among home environments is proposed. Our approach goes far beyond traditional systems for distributed virtualization, like PlanetLab or grid computing, as it relies on complete decentralization in a peer-to-peer (P2P) like manner and, above all, aims at energy efficiency. Energy metrics are defined, which have to be optimized by the system. The system itself uses virtualization to transparently move tasks from one home to another to optimally utilize the existing computing power. We present an overview of our proposed architecture, consisting of a middleware interconnecting computers and routers in possibly millions of homes using P2P techniques. For demonstrating the potential energy saving of distributed applications, we present an analytical model for sharing downloads, which is verified by discrete event simulation. The model represents an optimistic case without P2P overhead and fairness. The model allows to assess the upper limit of the saving potential. An enhanced version of the simulation model also shows the effect of fairness. The fairer the system gets, the less efficient it is.

e existing electromobility (EM) is still in its edgling stage and multiple challenges have to be overcome to make Electric Vehicles (EVs) as convenient as combustion engine vehicles. Users and Electric Vehicle Fleet Operators (EFOs) want their EVs to be charged and ready for use at all times. is straightforward goal, however, is counteracted from various sides: e range of the EV depends on the status and depletion of the EV ba ery which is in uenced by EV use and charging characteristics. Also, most convenient charging from the user's point of view, might unfortunately lead to problems in the power grid. As in the case of a power peak in the evening when EV users return from work and simultaneously plug in their EVs for charging. Last but not least, the mass of EV ba eries are an untapped potential to store electricity from intermi ent renewable energy sources. In this paper, we propose a novel approach to tackle this multilayered problem from di erent perspectives. Using on-board EV data and grid prediction models, we build up an information model as a foundation for a back end service containing EFO and Charging Station Provider (CSP) logic as well as a central Advanced Drivers Assistant System (ADAS). ese components connect to both battery management and user interfaces suggesting various routing and driving behaviour alternatives customized and incentivized for the current user pro le optimizing above mentioned goals. CCS CONCEPTS •Applied computing →Transportation; •Hardware →Smart grid; Energy distribution; •Social and professional topics →User characteristics; •So ware and its engineering →So ware architectures;

In this paper, a new architecture for sharing resources amongst horne environments is proposed. Our approach goes far beyond traditional systems for distributed virtualization like Planetlab or Grid computing, since it relies on complete decentralization in a peer-to-peer Iike manner, and above all, aims at energy efficiency. Energy metrics are defined, which have to bc optimized by the system. The system itself uses virtualization to transparently move tasks from one horne to another in order to optimally utilize the existing computing power. An overview of our proposed architecture is presented as weil as an analytical evaluation of the possible energy savings in a distributcd example scenario where computers share downloads.

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