Access to reliable electric power is considered by the developed world to be a minimum requirement for a reasonable standard of living. In addition to meeting a fluctuating demand, the modern electricity industry must now integrate intermittent generation sources like wind into the grid. Reserve margin allocation (RMA) for an acceptable loss of load expectation (LOLE) allows traditional generators to maintain grid reliability in the presence of small penetrations of wind energy. However, traditional RMA over-allocates the reserve capacity in the presence of short-term intermittency mitigation techniques like energy storage and demand response. For economic operation of the modern, grid better characterization techniques are needed for reserve margin reduction behavior in the presence of wind energy. This thesis addresses this challenge with a quantitative RMA analysis using real-world and simulated wind data for three different grid scenarios, with and without intermittency mitigation. The research is novel in its first-principles approach and its investigation into the practical validity of the analogy between demand response and energy response.

With the total energy consumption of computing systems increasing at a steep rate, much attention had been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving the performance of query processing. The objective of this study is to explore the potential of energy conservation in relational database management systems. The hypothesis is: by modifying the query optimizer in a Database management system (DBMS) to take the energy cost of query plans into consideration, we will be able to reduce the energy usage of database servers and control the tradeoffs between energy consumption and system performance. In this thesis, we provide an in-depth anatomy of typical queries in various benchmarks and qualitatively analyze the energy profile of such queries. The results of extensive experiments show that power savings in the range of 11% to 22% can be achieved by equipping the DBMS with a simple query optimizer that selects query plans based on both estimated processing time and energy requirements. We advocate more research efforts be invested into the design and evaluation of power-aware DBMSs in hope to reach higher level of energy efficiency.

The roll-out of the fifth-generation (5G) wireless networks alongside existing generations and characterized by a dense deployment of base stations (BSs) to serve an ever-increasing number of users and services leads to a drastic increase in the overall network energy consumption (EC). It can lead to an unprecedented rise in operational expenditure (OPEX) for the network operators and an increased global carbon footprint. The present-day networks are dimensioned according to the peak traffic demands, and hence are under-utilized due to the daily traffic variations. Therefore, to save energy, BSs can be put into sleep with different levels following the daily load variations. Selection of the right sleep level at the right instant is important to adapt the availability of the resources to the traffic load to maximize the energy savings without degrading the performance of the network. Previous studies focused on the selection of sleep modes (SMs) to maximize energy saving or the sleep duration given configuration and network resources. However, adaptive BS configuration together with SMs have not been investigated. In this thesis, the goal is to consider the design of the wireless network resources to cover an area with a given traffic demand in combination with sleep mode management. To achieve this, a novel EC model is proposed to capture the activity time of a 5G BS in a multi-cell environment. The activity factor of a BS is defined as the fraction of time the BS is transmitting over a fixed period and is dependent on the amount of BS resources. The new model captures the variation in power consumption by configuring three BS resources: 1) the active array size, 2) the bandwidth, and 3) the spatial multiplexing factor. We then implement a Q-learning algorithm to adapt these resources following the traffic demand and also the selection of sleep levels. Our results show that the difference in the average daily EC of BSs considered can be as high as 60% depending on the deployment area. Furthermore, the EC of a BS can be reduced by 57% during the low traffic hours by having deeper sleep levels as compared to the baseline scenario with no sleep modes. Implementing the resource adaptation algorithm further reduces the average EC of the BS by up to 20% as compared to the case without resource adaptation. However, the EE gain obtained by the algorithm depends on its convergence, which varies with the distribution of the users in the cell, the peak traffic demand, and the BS resources available. Our results show that by combining resource adaptation with deep sleep levels, one can obtain significant energy savings under variable traffic load. However, to ensure the reliability of the results obtained, we emphasize the need to guarantee the convergence of the algorithm before its use for resource adaptation. / Under de senaste åren har intresset för energieffektivitet (EE) av mobila kommunikationssystem ökat på grund av den ökande energiförbrukningen (EF). Med femte generationens mobilsystem, vilket kännetecknas av mer komplexa och kraftfulla basstationer (BS) för att betjäna ett ständigt ökande antal användare och tjänster, riskerar nätverkets totala EF att öka ytterligare. Detta kan leda till en markant ökning av operativa utgifter (OPEX) för nätoperatörerna och ett ökat globalt koldioxidavtryck. Många studier har visat att dagens nätverk ofta är överdimensionerade och att radioresurserna är underutnyttjade på grund av variationerna i det dagliga trafikbehovet. Genom att anpassa BS radioresurser efter trafikbehovet kan man säkerställa att man uppfyller användarkraven samtidigt som man minskar den totala EF. I denna studie föreslås en aktivitetsbaserad metod för att utvärdera EF för en BS. Aktivitetsfaktorn för en BS definieras som den bråkdel av tiden som BS är aktiv (sänder data) under en fast period och är beroende av mängden radioresurser. För att kvantifiera EF för en BS föreslås en ny modell som beräknar in effekt till BS som funktion av utstrålad effekt från BS. Den nya modellen fångar variationen i energiförbrukning med tre huvudsakliga radioresurser som är: 1) antal sändarantenner 2) bandbredd och 3) den spatiella multiplexingfaktorn (antal användare som schemaläggs samtidigt). Därefter implementeras en Q- inlärningsalgoritm för att anpassa dessa resurser efter det upplevda trafikbehovet och vilolägen som BS kan växla till när den är inaktiv. Ett viloläge innebär att viss hårdvara i BS stängs av. Resultatet visar att man genom att identifiera rätt typ av BS utifrån lokala trafikförhållanden kan få energibesparingar så höga som 60%. Vidare kan EF för en BS reduceras med 57% under den tid av dygnet då trafiken är som lägst genom att ha djupare vilolägen jämfört med basscenariot utan vilolägen. Genom att implementera Q-inlärningsalgoritmen som anpassar tillgängliga radioresurser till trafikbehovet minskar den genomsnittliga EF för BS ytterligare med upp till 20%. Vinsten i EE som erhålls av algoritmen beror dock till stor del på dess konvergens, som varierar med fördelningen av användarna i cellen, topptrafikbehovet och BS tillgängliga radioresurser. Resultatet visar att genom att kombinera resursanpassning med vilolägen kan man få betydande energibesparingar under varierande trafikbelastning. För att säkerställa tillförlitligheten av de erhållna resultaten betonas emellertid behovet av att garantera konvergensen av algoritmen innan den används för resursanpassning.

High-performance computing (HPC) systems have become power limited. For instance, the U.S. Department of Energy set a power envelope of 20MW in 2008 for the first exascale supercomputer now expected to arrive in 2021--22. Toward this end, we seek to improve the greenness of HPC systems by improving their performance per watt at the allocated power budget. In this dissertation, we develop a series of models and techniques to manage power at micro-, meso-, and macro-levels of the system hierarchy, specifically addressing data movement and heterogeneity. We target the chip interconnect at the micro-level, heterogeneous nodes at the meso-level, and a supercomputing cluster at the macro-level. Overall, our goal is to improve the greenness of HPC systems by intelligently managing power. The first part of this dissertation focuses on measurement and modeling problems for power. First, we study how to infer chip-interconnect power by observing the system-wide power consumption. Our proposal is to design a novel micro-benchmarking methodology based on data-movement distance by which we can properly isolate the chip interconnect and measure its power. Next, we study how to develop software power meters to monitor a GPU's power consumption at runtime. Our proposal is to adapt performance counter-based models for their use at runtime via a combination of heuristics, statistical techniques, and application-specific knowledge. In the second part of this dissertation, we focus on managing power. First, we propose to reduce the chip-interconnect power by proactively managing its dynamic voltage and frequency (DVFS) state. Toward this end, we develop a novel phase predictor that uses approximate pattern matching to forecast future requirements and in turn, proactively manage power. Second, we study the problem of applying a power cap to a heterogeneous node. Our proposal proactively manages the GPU power using phase prediction and a DVFS power model but reactively manages the CPU. The resulting hybrid approach can take advantage of the differences in the capabilities of the two devices. Third, we study how in-situ techniques can be applied to improve the greenness of HPC clusters. Overall, in our dissertation, we demonstrate that it is possible to infer power consumption of real hardware components without directly measuring them, using the chip interconnect and GPU as examples. We also demonstrate that it is possible to build models of sufficient accuracy and apply them for intelligently managing power at many levels of the system hierarchy. / Doctor of Philosophy / Past research in green high-performance computing (HPC) mostly focused on managing the power consumed by general-purpose processors, known as central processing units (CPUs) and to a lesser extent, memory. In this dissertation, we study two increasingly important components: interconnects (predominantly focused on those inside a chip, but not limited to them) and graphics processing units (GPUs). Our contributions in this dissertation include a set of innovative measurement techniques to estimate the power consumed by the target components, statistical and analytical approaches to develop power models and their optimizations, and algorithms to manage power statically and at runtime. Experimental results show that it is possible to build models of sufficient accuracy and apply them for intelligently managing power on multiple levels of the system hierarchy: chip interconnect at the micro-level, heterogeneous nodes at the meso-level, and a supercomputing cluster at the macro-level.

Networks-on-Chip (NoCs) are a general purpose, scalable replacement for shared medium wired interconnects offering many practical applications in industry. Dynamic Voltage Frequency Scaling (DVFS) is a technique whereby a chip?s voltage-frequency levels are varied at run time, often used to conserve dynamic power. Various DVFSbased NoC optimization techniques have been proposed. However, due to the resources required to validate architectural decisions through prototyping, few are implemented. As a result, designers are faced with a lack of insight into potential power savings or performance gains at early architecture stages. This thesis proposes a DVFS aware NoC simulator with support for per node power-frequency modeling to allow fine-tuning of such optimization techniques early on in the design cycle. The proposed simulator also provides a framework for benchmarking various candidate strategies to allow selective prototyping and optimization. As part of the research, DVFS extensions were built for an existing NoC performance simulator and released for public use. This thesis presents some of the preliminary results from our simulator that show the average power consumed per node for all the benchmarks in SPLASH 2 benchmark suite [74] to be quite similar to each other. This thesis also serves as a technical manual for the simulator extensions. Important links for downloading and using the simulator are provided at the end of this document in Appendix C.

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This work presents improvements to a multi-core performance/power simulator. The improvements which include updated power models, voltage scaling aware models, and an application specific benchmark, are done to increase the accuracy of power models under voltage and frequency scaling. Improvements to the simulator enable more accurate design space exploration for a biomedical application. The work flow used to modify the simulator is also presented so similar modifications could be used on future simulators. / Graduation date: 2012