The overarching goal of this thesis is to investigate network architectures, and find the trade-off between low overall energy use and maintaining the level of quality of service (QoS), or even improve it. The ubiquitous wireless communications environment supports the exploration of different network architectures and techniques, the so-called heterogeneous network. Two kinds of heterogeneous architectures are considered: a combined cellular and femtocell network and a combined cellular, femtocell and Wireless Local Area Network(WLAN) network. This thesis concludes that the investigated heterogeneous networks can significantly reduce the overall power consumption, depending on the uptake of femtocells and WLANs. Also, QoS remains high when the power consumption drops. The main energy saving is from reducing the macrocell base station embodied and operational energy. When QoS is evaluated based on the combined cellular and femtocell architecture, it is suggested that use of resource scheduling for femtocells within the macrocell is crucial since femtocell performance is affected significantly by interference when installed in a co-channel system. Additionally, the femtocell transmission power mode is investigated using either variable power level or a fixed power level. To achieve both energy efficiency and QoS, the choice of system configurations should change according to the density of the femtocell deployment. When combining deployment of femtocells with WLANs, more users are able to experience a higher QoS. Due to increasing of data traffic and smartphone usage in the future, WLANs are more important for offloading data from the macrocell, reducing power consumption and also increasing the bandwidth. The localised heterogeneous network is a promising technique for achieving power efficiency and a high QoS system.

The building sector accounts for 40 % of the energy usage, and to be able to reach the energy reduction goals set within the EU and Sweden, the building sector needs to change toward energy efficiency. The building sector has a lot of energy saving potential, and within the sector, the HVAC- and plumbing section has the greatest saving potential. Since building trade magazines are directed to practitioners within the building sector, and also their main channel for information regarding projects and developments, this study has used qualitative content analysis on articles, regarding energy efficiency, between the years of 2002-2014. Two building magazines and one HVAC- and plumbing magazine were used to cover the field of what issues regarding energy efficiency were mediated to the readers. The study found out that during the first years, the magazines mediated a positive image towards energy efficiency measures, but mentioned little concrete action plans. After the implementation of more stringent laws, the magazines mediated different issues in a more equal spreading, however two different issues battled to be heard. These were concerns regarding the risks of using new methods not tried before, the lack of clear definitions from the authorities and a focus on a holistic perspective that included environmental thinking. The most recent years focused on practical solutions, adopting a holistic perspective that included both buildings and individual behaviors. During the years, the magazines in general framed energy efficiency measures as something positive and mediated the image of that energy efficiency measures would be taken in the future.

De nos jours, de nombreuses entreprises choisissent de plus en plus d'adopter le cloud computing. Plus précisément, en tant que clients, elles externalisent la gestion de leur infrastructure physique vers des centres de données (ou plateformes de cloud computing). La consommation d'énergie est une préoccupation majeure pour la gestion des centres de données (datacenter, DC). Son impact financier représente environ 80% du coût total de possession et l'on estime qu'en 2020, les DCs américains dépenseront à eux seuls environ 13 milliards de dollars en factures énergétiques. Généralement, les serveurs de centres de données sont conçus de manière à atteindre une grande efficacité énergétique pour des utilisations élevées. Pour diminuer le coût de calcul, les serveurs de centre de données devraient maximiser leur utilisation. Afin de lutter contre l'utilisation historiquement faible des serveurs, le cloud computing a adopté la virtualisation des serveurs. Cette dernière permet à un serveur physique d'exécuter plusieurs serveurs virtuels (appelés machines virtuelles) de manière isolée. Avec la virtualisation, le fournisseur de cloud peut regrouper (consolider) l'ensemble des machines virtuelles (VM) sur un ensemble réduit de serveurs physiques et ainsi réduire le nombre de serveurs actifs. Même ainsi, les serveurs de centres de données atteignent rarement des utilisations supérieures à 50%, ce qui signifie qu'ils fonctionnent avec des ensembles de ressources majoritairement inutilisées (appelés «trous»). Ma première contribution est un système de gestion de cloud qui divise ou fusionne dynamiquement les machines virtuelles de sorte à ce qu'elles puissent mieux remplir les trous. Cette solution n'est efficace que pour des applications élastiques, c'est-à-dire des applications qui peuvent être exécutées et reconfigurées sur un nombre arbitraire de machines virtuelles. Cependant, la fragmentation des ressources provient d'un problème plus fondamental. On observe que les applications cloud demandent de plus en plus de mémoire, tandis que les serveurs physiques fournissent plus de CPU. Dans les DC actuels, les deux ressources sont fortement couplées puisqu'elles sont liées à un serveur physique. Ma deuxième contribution est un moyen pratique de découpler la paire CPU-mémoire, qui peut être simplement appliquée à n'importe quel serveur. Ainsi, les deux ressources peuvent varier indépendamment, en fonction de leur demande. Ma troisième et ma quatrième contribution montrent un système pratique qui exploite la deuxième contribution. La sous-utilisation observée sur les serveurs physiques existe également pour les machines virtuelles. Il a été démontré que les machines virtuelles ne consomment qu'une petite fraction des ressources allouées car les clients du cloud ne sont pas en mesure d'estimer correctement la quantité de ressources nécessaire à leurs applications. Ma troisième contribution est un système qui estime la consommation de mémoire (c'est-à-dire la taille du working set) d'une machine virtuelle, avec un surcoût faible et une grande précision. Ainsi, nous pouvons maintenant consolider les machines virtuelles en fonction de la taille de leur working set (plutôt que leur mémoire réservée). Cependant, l'inconvénient de cette approche est le risque de famine de mémoire. Si une ou plusieurs machines virtuelles ont une forte augmentation de la demande en mémoire, le serveur physique peut manquer de mémoire. Cette situation n'est pas souhaitable, car la plate-forme cloud est incapable de fournir au client la mémoire qu'il a payée. Finalement, ma quatrième contribution est un système qui permet à une machine virtuelle d'utiliser la mémoire distante fournie par un autre serveur du rack. Ainsi, dans le cas d'un pic de la demande en mémoire, mon système permet à la VM d'allouer de la mémoire sur un serveur physique distant. / Nowadays, many organizations choose to increasingly implement the cloud computing approach. More specifically, as customers, these organizations are outsourcing the management of their physical infrastructure to data centers (or cloud computing platforms). Energy consumption is a primary concern for datacenter (DC) management. Its cost represents about 80% of the total cost of ownership and it is estimated that in 2020, the US DCs alone will spend about $13 billion on energy bills. Generally, the datacenter servers are manufactured in such a way that they achieve high energy efficiency at high utilizations. Thereby for a low cost per computation all datacenter servers should push the utilization as high as possible. In order to fight the historically low utilization, cloud computing adopted server virtualization. The latter allows a physical server to execute multiple virtual servers (called virtual machines) in an isolated way. With virtualization, the cloud provider can pack (consolidate) the entire set of virtual machines (VMs) on a small set of physical servers and thereby, reduce the number of active servers. Even so, the datacenter servers rarely reach utilizations higher than 50% which means that they operate with sets of longterm unused resources (called 'holes'). My first contribution is a cloud management system that dynamically splits/fusions VMs such that they can better fill the holes. This solution is effective only for elastic applications, i.e. applications that can be executed and reconfigured over an arbitrary number of VMs. However the datacenter resource fragmentation stems from a more fundamental problem. Over time, cloud applications demand more and more memory but the physical servers provide more an more CPU. In nowadays datacenters, the two resources are strongly coupled since they are bounded to a physical sever. My second contribution is a practical way to decouple the CPU-memory tuple that can simply be applied to a commodity server. Thereby, the two resources can vary independently, depending on their demand. My third and my forth contribution show a practical system which exploit the second contribution. The underutilization observed on physical servers is also true for virtual machines. It has been shown that VMs consume only a small fraction of the allocated resources because the cloud customers are not able to correctly estimate the resource amount necessary for their applications. My third contribution is a system that estimates the memory consumption (i.e. the working set size) of a VM, with low overhead and high accuracy. Thereby, we can now consolidate the VMs based on their working set size (not the booked memory). However, the drawback of this approach is the risk of memory starvation. If one or multiple VMs have an sharp increase in memory demand, the physical server may run out of memory. This event is undesirable because the cloud platform is unable to provide the client with the booked memory. My fourth contribution is a system that allows a VM to use remote memory provided by a different rack server. Thereby, in the case of a peak memory demand, my system allows the VM to allocate memory on a remote physical server.

This thesis aims to examine energy demand within UK industry and assess the improvement potential available through efficiency measures. The techniques employed throughout the work have been mainly engineering based, drawing on thermodynamics. Alongside this approach, an assessment of drivers and barriers to the technical potential was undertaken. Data availability was a key challenge in the current work. The variety in energy uses meant the use of publically available datasets was limited. A database was constructed utilising site level emissions data, and employed a subsector disaggregation that facilitated energy analysis. The database was used for an analysis of waste heat recovery options. Opportunities were identified in low temperature recovery, heat-ta-power technology, and the transport of heat. Each of these options would require further research and support to be fully realised. It was found that splitting the industrial sector into an energy-intensive and non-energy- intensive subsector, where the grouping was based on the drivers to energy efficiency, allowed generalisations to be made regarding future improvement potential. Based on analysis of past trends, it was found that the energy-intensive subsector has limited potential for further efficiency gains through currently used processes. To make significant improvements radical changes in current processes will be required. A study of the energy-intensive Cement subsector concurred with these findings. Future efficiency improvements in this subsector are likely limited without a shift to alternative cement production. The non-energy-intensive subsector was thought to have relatively greater improvement potential through existing processes. The analysis of these processes is limited by lack of data however. An analysis of the non-energy-intensive Food and drink subsector therefore focussed on improvements in supplying low temperature heat, rather than the efficiency of specific processes. Opportunities through improving steam systems, increasing combined heat-and-power use, and the adoption of heat pumps were found to offer similar improvement potentials.

Managing energy demand is essential to energy security and climate change mitigation. The industrial sector accounts for over a fifth of UK primary energy demand and greenhouse gas emissions. Energy intensive industry is uniquely restricted in the way it uses energy and emits greenhouse gasses. In this thesis, the potential of radical measures to achieve significant energy demand reduction and emissions abatement in UK energy intensive industry is assessed. Adopted is a multidisciplinary approach combining thermodynamic and techno-economic analysis techniques. Bottom-up assessments are applied to key energy intensive sectors of industry to capture the diverse and interactive array of technological characteristics invisible from a top-down perspective. Detailed projection models are built to design and analyse technology roadmaps for the sectors out to 2050. In an illustrative roadmap assessment, the technological pathways of radical process transition and carbon sequestration were each shown to achieve about 80% abatement in 2050 from 1990 emissions levels. Radical process transition achieved greater abatement before 2030 and this was reflected in lower cumulative emissions over the full period. Higher risk is associated with carbon sequestration from its reliance on timely access to CO2 transport and storage technology to compensate for lower short-medium term abatement. Although, combining carbon sequestration with high levels of biomass combustion indicated the largest potential abatement to 2050. Abatement economics in the iron and steel sector are notably sensitive to resource costs and the carbon trading price. The carbon trading price influences relative production costs in favour of higher abating pathways, but increases absolute costs. This signals the need for supportive policy measures that accelerate technology development and deployment while mitigating the risk of the carbon trading price to competitiveness. Some carbon capture technologies reduce relative production cost even in the absence of a carbon price, but this excludes the cost of CO2 transport and storage. Meanwhile, radical process transition pathways have a higher dependence on the future prices of natural gas, electricity, and scrap. Future work should focus on expanding the economic appraisal to other sectors and to indirect costs, as well as incorporating wider material efficiency strategies and running different future scenarios.

Os atuais sistemas de HPC tem realizado simulações mais complexas possíveis, produzindo benefícios para diversas áreas de pesquisa. Para atender à crescente demanda de processamento dessas simulações, novos equipamentos estão sendo projetados, visando à escala exaflops. Um grande desafio para a construção destes sistemas é a potência que eles vão demandar, onde perspectivas atuais alcançam GigaWatts. Para resolver este problema, esta tese apresenta uma abordagem para aumentar a eficiência energética usando recursos de HPC, objetivando reduzir os efeitos do desequilíbrio de carga e economizar energia. Nós desenvolvemos uma estratégia baseada no consumo de energia, chamada ENERGYLB, que considera características da plataforma, irregularidade e dinamicidade de carga das aplicações para melhorar a eficiência energética. Nossa estratégia leva em conta carga computacional atual e a frequência de clock dos cores, para decidir entre chamar uma estratégia de balanceamento de carga que reduz o desequilíbrio de carga migrando tarefas, ou usar técnicas de DVFS par ajustar as frequências de clock dos cores de acordo com suas cargas computacionais ponderadas. Como as diferentes arquiteturas de processador podem apresentam dois níveis de granularidade de DVFS, DVFS-por-chip ou DVFS-por-core, nós criamos dois diferentes algoritmos para a nossa estratégia. O primeiro, FG-ENERGYLB, permite um controle fino da frequência dos cores em sistemas que possuem algumas dezenas de cores e implementam DVFS-por-core. Por outro lado, CG-ENERGYLB é adequado para plataformas de HPC composto de vários processadores multicore que não permitem tal refinado controle, ou seja, que só executam DVFS-por-chip. Ambas as abordagens exploram desbalanceamentos residuais em aplicações interativas e combinam balanceamento de carga dinâmico com técnicas de DVFS. Assim, eles reduzem a frequência de clock dos cores com menor carga computacional os quais apresentam algum desequilíbrio residual mesmo após as tarefas serem remapeadas. Nós avaliamos a aplicabilidade das nossas abordagens utilizando o ambiente de programação paralela CHARM++ sobre benchmarks e aplicações reais. Resultados experimentais presentaram melhorias no consumo de energia e na demanda potência sobre algoritmos do estado-da-arte. A economia de energia com ENERGYLB usado sozinho foi de até 25% com nosso algoritmo FG-ENERGYLB, e de até 27% com nosso algoritmo CG-ENERGYLB. No entanto, os desequilíbrios residuais ainda estavam presentes após as serem tarefas remapeadas. Neste caso, quando as nossas abordagens foram empregadas em conjunto com outros balanceadores de carga, uma melhoria na economia de energia de até 56% é obtida com FG-ENERGYLB e de até 36% com CG-ENERGYLB. Estas economias foram obtidas através da exploração do desbalanceamento residual em aplicações interativas. Combinando balanceamento de carga dinâmico com DVFS nossa estratégia é capaz de reduzir a demanda de potência média dos sistemas paralelos, reduzir a migração de tarefas entre os recursos disponíveis, e manter o custo de balanceamento de carga baixo. / Current HPC systems have made more complex simulations feasible, yielding benefits to several research areas. To meet the increasing processing demands of these simulations, new equipment is being designed, aiming at the exaflops scale. A major challenge for building these systems is the power that they will require, which current perspectives reach the GigaWatts. To address this problem, this thesis presents an approach to increase the energy efficiency using of HPC resources, aiming to reduce the effects of load imbalance to save energy. We developed an energy-aware strategy, called ENERGYLB, which considers platform characteristics, and the load irregularity and dynamicity of the applications to improve the energy efficiency. Our strategy takes into account the current computational load and clock frequency, to decide whether to call a load balancing strategy that reduces load imbalance by migrating tasks, or use Dynamic Voltage and Frequency Scaling (DVFS) technique to adjust the clock frequencies of the cores according to their weighted loads. As different processor architectures can feature two levels of DVFS granularity, per-chip DVFS or per-core DVFS, we created two different algorithms for our strategy. The first one, FG-ENERGYLB, allows a fine control of the clock frequency of cores in systems that have few tens of cores and feature per-core DVFS control. On the other hand, CGENERGYLB is suitable for HPC platforms composed of several multicore processors that do not allow such a fine-grained control, i.e., that only perform per-chip DVFS. Both approaches exploit residual imbalances on iterative applications and combine dynamic load balancing with DVFS techniques. Thus, they reduce the clock frequency of underloaded computing cores, which experience some residual imbalance even after tasks are remapped. We evaluate the applicability of our approaches using the CHARM++ parallel programming system over benchmarks and real world applications. Experimental results present improvements in energy consumption and power demand over state-of-the-art algorithms. The energy savings with ENERGYLB used alone were up to 25%with our FG-ENERGYLB algorithm, and up to 27%with our CG-ENERGYLB algorithm. Nevertheless, residual imbalances were still present after tasks were remapped. In this case, when our approaches were employed together with these load balancers, an improvement in energy savings of up to 56% is achieved with FG-ENERGYLB and up to 36% with CG-ENERGYLB. These savings were obtained by exploiting residual imbalances on iterative applications. By combining dynamic load balancing with the DVFS technique, our approach is able to reduce the average power demand of parallel systems, reduce the task migration among the available resources, and keep load balancing overheads low.

Multiple-input multiple-output (MIMO) systems are a mature technology that has been incorporated into current wireless broadband standards to improve the channel capacity and link reliability. Nevertheless, due to the continuous increasing demand for wireless data traffic new strategies are to be adopted. Very large MIMO antenna arrays represents a paradigm shift in terms of theory and implementation, where the use of tens or hundreds of antennas provides significant improvements in throughput and radiated energy efficiency compared to single antennas setups. Since design constraints limit the number of usable antennas, virtual systems can be seen as a promising technique due to their ability to mimic and exploit the gains of multi-antenna systems by means of wireless cooperation. Considering these arguments, in this work, energy efficient coding and network design for large virtual MIMO systems are presented. Firstly, a cooperative virtual MIMO (V-MIMO) system that uses a large multi-antenna transmitter and implements compress-and-forward (CF) relay cooperation is investigated. Since constructing a reliable codebook is the most computationally complex task performed by the relay nodes in CF cooperation, reduced complexity quantisation techniques are introduced. The analysis is focused on the block error probability (BLER) and the computational complexity for the uniform scalar quantiser (U-SQ) and the Lloyd-Max algorithm (LM-SQ). Numerical results show that the LM-SQ is simpler to design and can achieve a BLER performance comparable to the optimal vector quantiser. Furthermore, due to its low complexity, U-SQ could be consider particularly suitable for very large wireless systems. Even though very large MIMO systems enhance the spectral efficiency of wireless networks, this comes at the expense of linearly increasing the power consumption due to the use of multiple radio frequency chains to support the antennas. Thus, the energy efficiency and throughput of the cooperative V-MIMO system are analysed and the impact of the imperfect channel state information (CSI) on the system's performance is studied. Finally, a power allocation algorithm is implemented to reduce the total power consumption. Simulation results show that wireless cooperation between users is more energy efficient than using a high modulation order transmission and that the larger the number of transmit antennas the lower the impact of the imperfect CSI on the system's performance. Finally, the application of cooperative systems is extended to wireless self-backhauling heterogeneous networks, where the decode-and-forward (DF) protocol is employed to provide a cost-effective and reliable backhaul. The associated trade-offs for a heterogeneous network with inhomogeneous user distributions are investigated through the use of sleeping strategies. Three different policies for switching-off base stations are considered: random, load-based and greedy algorithms. The probability of coverage for the random and load-based sleeping policies is derived. Moreover, an energy efficient base station deployment and operation approach is presented. Numerical results show that the average number of base stations required to support the traffic load at peak-time can be reduced by using the greedy algorithm for base station deployment and that highly clustered networks exhibit a smaller average serving distance and thus, a better probability of coverage.

Rapid device-miniaturization keeps on inducing challenges in building energy efficient microprocessors. As the size of the transistors continuously decreasing, more uncertainties emerge in their operations. On the other hand, integrating more and more transistors on a single chip accentuates the need to lower its supply-voltage. This dissertation investigates one of the primary device uncertainties - timing error, in microprocessor performance bottleneck in NTC era. Then it proposes various innovative techniques to exploit these opportunities to maintain processor energy efficiency, in the context of emerging challenges. Evaluated with the cross-layer methodology, the proposed approaches achieve substantial improvements in processor energy efficiency, compared to other start-of-art techniques.

This thesis seeks to address energy efficient design in a temperate climate in typical small, medium density housing, particularly in housing for the aged. The connections between energy efficient design and small, medium density housing were identified as contemporary issues related to Australian Government policies in two disparate areas. One policy area is reflected in the Government's commitment to assist older people, whether they are active, early retirees or the frail elderly, wealthy or poor, to live in their chosen place of residence. Increasingly this chosen place of residence may be a small, medium density dwelling. The other policy area is that related to reducing energy consumption in buildings. This policy is reflected in recently proclaimed building regulations aimed at reducing space heating/cooling requirements in housing. The building regulations include details of acceptable construction practice for energy efficiency that may not be appropriate in small, medium density housing. It was proposed in this thesis that extensive use of space heating and cooling in housing for the aged was required because well-established benchmarks for energy efficient design in a temperate climate were not generally appropriate in small, medium density dwellings and were particularly inappropriate in housing for the aged. `Appropriate' in this context referred to: indoor temperatures being acceptable without the need for space heating and cooling; retaining the site planning and general form of typical, medium density aged persons housing developments in suburban Australia; cost effectiveness over the life of a building; and fitting the needs of physically and financially vulnerable older people. / The methods used to examine the notion of appropriateness commenced with a literature review that related to the general physical and economic status of older people and their needs and responses to space heating and cooling in the home. Further, the literature review considered the principles of energy efficient design and benchmark criteria for energy efficiency. Arising from the literature review, two tools of study were used in order to develop a set of data encapsulating the salient features of small, medium density housing. The first was a multiple case study of typical housing for the aged. This was conceived as a way of determining if small, medium density dwellings could provide appropriate indoor thermal conditions and/or were designed to be energy efficient. The indoor temperatures were monitored in summer and winter and annual energy consumption was established and statistically analysed. The building designs were analysed in terms of their orientation, glazing areas, wall areas, volumes of thermal mass and ventilation capacity and compared with benchmarks for energy efficient design. The second tool involved a series of computer simulations of a typical small, medium density dwelling. The simulation process was utilised to determine if a new set of benchmarks for energy efficient small, medium density dwellings were required that would incorporate the notion of appropriateness. From the multiple case study it was found that, irrespective of design, indoor temperatures in 98% of dwellings were above the acceptable maximum summer temperature of 27.4°C in still air and indoor temperatures in all dwellings were found to be below the acceptable minimum daytime temperature of 19.8°C. / The findings also showed that some aspects of the benchmarks for energy efficient design were not appropriate in typical, medium density housing constructed specifically for the aged. From the simulation process it was discovered that acceptable temperatures could be achieved in small medium density housing if the principles of energy efficient design, incorporated within a new set of benchmarks, were integrated with appropriateness criteria for housing for the aged. The approach taken with the new benchmarks was to create both performance based and prescriptive design solutions. The performance model differs from the current benchmarks for energy efficient design in that it establishes key functional objectives for energy efficient design. Compared to the current benchmarks, the prescriptive design solutions show significant reductions in the areas of northerly glazing and total glazing. To compensate for the reduced area of northerly glazing, both direct and indirect means of solar gain are utilised for passive heating. The thesis outcomes have implications for three areas of the construction industry. The prescriptive design solutions presented in building regulations for energy efficiency in housing need to be qualified, the design briefs prepared for energy efficient construction of small, medium density housing need amendment and the approach taken by designers involved in energy efficient small, medium density housing needs to be reconsidered.

The issue of sustainability has gained significance in the past two decades or so particularly inthe business sector throughout the globe. Companies from various industries have inculcatedaspects of sustainability in their portfolios due to reasons ranging from stakeholder demandsto industry competition and profit. In fact, with the issues of climate change and resourcedepletion now gaining global political and economic significance; not going ‘green’ is not anoption for companies anymore.This paper sheds light on the ways that companies are becoming more environmental friendlyand benefitting from this process. All aspects of the business process ranging fromprocurement of materials to waste management and recycling have been viewed from asustainability perspective. This also includes the manner in which firms practice ‘greenmarketing’ for their environmental friendly practices and products. Since this paper will actas a guiding strategy for a company ‘Ascom’, the industry under focus is thetelecommunications industry. More specifically, the activities of three of Ascom’s majorcompetitors namely Siemens, Alcatel and Cisco have been thoroughly reviewed to providebenchmarks for Ascom. Coupled with the current theories and frameworks on the issue ofsustainability in business, the benchmarks set by these competitors will be used torecommend Ascom on how they can become more ‘green’.