TRIAD - Preliminary design of an operational earth resources survey system Final report

Design of operational earth resources survey syste

The Water Footprint of Data Centers

The internet and associated Information and Communications Technologies (ICT) are diffusing at an astounding pace. As data centers (DCs) proliferate to accommodate this rising demand, their environmental impacts grow too. While the energy efficiency of DCs has been researched extensively, their water footprint (WF) has so far received little to no attention. This article conducts a preliminary WF accounting for cooling and energy consumption in DCs. The WF of DCs is estimated to be between 1047 and 151,061 m3/TJ. Outbound DC data traffic generates a WF of 1–205 liters per gigabyte (roughly equal to the WF of 1 kg of tomatos at the higher end). It is found that, typically, energy consumption constitues by far the greatest share of DC WF, but the level of uncertainty associated with the WF of different energy sources used by DCs makes a comprehensive assessment of DCs’ water use efficiency very challenging. Much better understanding of DC WF is urgently needed if a meaningful evaluation of this rapidly spreading service technology is to be gleaned and response measures are to be put into effect

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

The future of sustainable digital infrastructures: A landscape of solutions, adoption factors, impediments, open problems, and scenarios

Background: Digital infrastructures, i.e., ICT systems, or system-of-systems, providing digital capabilities, such as storage and computational services, are experiencing an ever-growing demand for data consumption, which is only expected to increase in the future. This trend leads to a question we need to answer: How can we evolve digital infrastructures to keep up with the increasing data demand in a sustainable way?Objective: The goal of this study is to understand what is the future of sustainable digital infrastructures, in terms of: which solutions are, or will be, available to sustainably evolve digital infrastructures, and which are the related adoption factors, impediments, and open problems.Method: We carried out a 3-phase mixed-method qualitative empirical study, comprising semi-structured interviews, followed by focus groups, and a plenary session with parallel working groups. In total, we conducted 13 sessions involving 48 digital infrastructure practitioners and researchers.Results: From our investigation emerges a landscape for sustainable digital infrastructures, composed of 30 solutions, 5 adoption factors, 4 impediments, and 13 open problems. We further synthesized our results in 4 incremental scenarios, which outline the future evolution of sustainable digital infrastructures.Conclusions: From an initial shift from on-premise to the cloud, as time progresses, digital infrastructures are expected to become increasingly distributed, till it will be possible to dynamically allocate resources by following time, space, and energy. Numerous solutions will support this change, but digital infrastructures are envisaged to be able to evolve sustainably only by (i) gaining a wider awareness of digital sustainability, (ii) holding every party accountable for their sustainability throughout value chains, and (iii) establishing cross-domain collaborations

From performance management to managing performance : An embedded case study of the drivers of individual- and group-based performance in a call center context

Managing performance is critical for realizing certain economic benefits when managing customer relations in call centers. However, prior call center research is fragmented and under-analyzed, which contributes to a limited understanding of the underlying elements for performance and complexities in managing individual- and group-based performance in call centers. The purpose of this thesis is to further our knowledge of how to manage performance in call centers.The findings from this qualitative study of four embedded cases in a Swedish company operating in the utilities sector provide empirical evidence of how call center agents and management manage performance. I propose that coping and the effects of coping strategies on performance constitute the primary link between contextual, control-based, cultural elements and performance outcomes. I found that call center agents handled their lack of knowledge of how to effectively solve (or not solve) a perceived problem by adopting various coping strategies. Such strategies were influenced by the amount of experienced coping over time and supported by dysfunctional prevailing performance-management systems. These coping strategies determined individual- and group-based performance in this call center setting.Based upon these findings, I suggest a more proactive role for middle managers in handling the underlying causes of these coping strategies, rather than their consequences, in terms of performance impacts. I also propose suggestions to management for handling internal challenges generated by a dysfunctional performance-management system in these call centers. I also provide additional managerial guidelines for managing customer relations and performance in call centers, such as how to align call center operations with company vision

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

Industrial demand-side flexibility:A key element of a just energy transition and industrial development

In many countries, industry is one of the largest consumers of electricity. Given the special importance of electricity for industry, a reliable electricity supply is a basic prerequisite for further industrial development and associated economic growth. As countries worldwide transition to a low-carbon economy (in particular, by the development of renewable energy sources), the increasing fluctuation in renewable energy production requires new flexibility options within the electricity system in order to guarantee security of supply. It is advanced in this paper that such a flexibility transition with an active participation of industry in general has unique potential: It will not only promote green industrial development, but also become an engine for inclusive industrial development and growth as well as delivering a just transition to a low-carbon economy. Given the high potential of industrial demand-side flexibility, a first monitoring approach for such a flexibility transition is illustrated, which bases on a flexibility index. Our flexibility index allows for an indication of mis-developments and supports an appropriate implementation of countermeasures together with relevant stakeholders. Hence, it holds various insights for both policy-makers and practice with respect to how industrial demand-side flexibility can ensure advances towards an inclusive, just, and sustainable industrial development

Hybrid Systems for Marine Energy Harvesting

Technologies to harvest marine renewable energies (MREs) are at a pre-commercial stage, and significant R&D progress is still required in order to improve their competitiveness. Therefore, hybridization presents a significant potential, as it fosters synergies among the different harvesting technologies and resources. In the scope of this Special Issue, hybridization is understood in three different manners: (i) combination of technologies to harvest different MREs (e.g., wave energy converters combined with wind turbines); (ii) combination of different working principles to harvest the same resource (e.g., oscillating water column with an overtopping device to harvest wave energy); or (iii) integration of harvesting technologies in multifunctional platforms and structures (e.g., integration of wave energy converters in breakwaters). This Special Issue presents cutting-edge research on the development and testing of hybrid technologies for harvesting MREs and intends to inform interested readers on the most recent advances in this key topic