Matching Items (3)
Filtering by
- All Subjects: Energy Efficiency
- Creators: Wu, Carole-Jean
Description
Energy consumption of the data centers worldwide is rapidly growing fueled by ever-increasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. This dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level. We devise optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and cooling system power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.
ContributorsAbbasi, Zahra (Author) / Gupta, Sandeep K. S. (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Shrivastava, Aviral (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many different levels of government, organizations, and programs actively shape the future of energy in Arizona, a state that lacks a comprehensive energy plan. Disparate actions by multiple actors may slow the energy policy process rather than expedite it. The absence of a state energy policy or plan raises questions about how multiple actors and ideas engage with state energy policy development and whether the absence of a comprehensive state plan can be understood. Improving how policy development is conceptualized and giving more focused attention to the mechanisms by which interested parties become involved in shaping Arizona energy policy. To explore these questions, I examine the future energy efficiency. Initially, public engagement mechanisms were examined for their role in policy creation from a theoretical perspective. Next a prominent public engagement forum that was dedicated to the topic of the Arizona's energy future was examined, mapping its process and conclusions onto a policy process model. The first part of this thesis involves an experimental expert consultation panel which was convened to amplify and refine the results of a public forum. The second part utilizes an online follow up survey to complete unfinished ideas from the focus group. The experiment flowed from a hypothesis that formal expert discussion on energy efficiency policies, guided by the recommendations put forth by the public engagement forum on energy in Arizona, would result in an increase in relevance while providing a forum for interdisciplinary collaboration that is atypical in today's energy discussions. This experiment was designed and evaluated utilizing a public engagement framework that incorporated theoretical and empirical elements. Specifically, I adapted elements of three methods of public and expert engagement used in policy development to create a consultation process that was contextualized to energy efficiency stakeholders in Arizona and their unique constraints. The goal of the consultation process was to refine preferences about policy options by expert stakeholders into actionable goals that could achieve advancement on policy implementation. As a corollary goal, the research set out to define implementation barriers, refine policy ideas, and operationalize Arizona-centric goals for the future of energy efficiency.
ContributorsBryck, Drew (Author) / Graffy, Elisabeth A. (Thesis advisor) / Dalrymple, Michael (Committee member) / Miller, Clark (Committee member) / Arizona State University (Publisher)
Created2013
Description
User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy.
Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.
Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.
ContributorsGaudette, Benjamin David (Author) / Vrudhula, Sarma (Thesis advisor) / Wu, Carole-Jean (Thesis advisor) / Fainekos, Georgios (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017