136,139 research outputs found
Electronics Thermal Management in Information and Communications Technologies: Challenges and Future Directions
This paper reviews thermal management challenges encountered in a wide range of electronics cooling applications from large-scale (data center and telecommunication) to smallscale systems (personal, portable/wearable, and automotive). This paper identifies drivers for progress and immediate and future challenges based on discussions at the 3rd Workshop on Thermal Management in Telecommunication Systems and Data Centers held in Redwood City, CA, USA, on November 4–5, 2015. Participants in this workshop represented industry and academia, with backgrounds ranging from data center thermal management and energy efficiency to high-performance computing and liquid cooling, thermal management in wearable and mobile devices, and acoustic noise management. By considering a wide range of electronics cooling applications with different lengths and time scales, this paper identifies both common themes and diverging views in the thermal management community
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Development of a Rooftop Collaborative Experimental Space through Experiential Learning Projects
The Solar, Water, Energy, and Thermal Laboratory
(SWEAT Lab) is a rooftop experimental space at the
University of Texas at Austin built by graduate and
undergraduate students in the Cockrell School of
Engineering. The project was funded by the Texas State
Energy Conservation Office and the University’s Green
Fee Grant, a competitive grant program funded by UT
Austin tuition fees to support sustainability-related projects
and initiatives on campus. The SWEAT Lab is an on-going
experiential learning facility that enables engineering
education by deploying energy and water-related projects.
To date, the lab contains a full weather station tracking
weather data, a rainwater harvesting system and rooftop
garden.
This project presented many opportunities for students to
learn first hand about unique engineering challenges. The
lab is located on the roof of the 10 story Engineering
Teaching Center (ETC) building, so students had to design
and build systems with constraints such as weight
limitations and wind resistance. Students also gained
experience working with building facilities and
management for structural additions, power, and internet
connection for instruments.
With the Bird’s eye view of UT Austin campus, this unique
laboratory offers a new perspective and dimension to
applied student research projects at UT Austin.Cockrell School of Engineerin
Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing
Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems.
We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies.
Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Cloud computing is offering utility-oriented IT services to users worldwide.
Based on a pay-as-you-go model, it enables hosting of pervasive applications
from consumer, scientific, and business domains. However, data centers hosting
Cloud applications consume huge amounts of energy, contributing to high
operational costs and carbon footprints to the environment. Therefore, we need
Green Cloud computing solutions that can not only save energy for the
environment but also reduce operational costs. This paper presents vision,
challenges, and architectural elements for energy-efficient management of Cloud
computing environments. We focus on the development of dynamic resource
provisioning and allocation algorithms that consider the synergy between
various data center infrastructures (i.e., the hardware, power units, cooling
and software), and holistically work to boost data center energy efficiency and
performance. In particular, this paper proposes (a) architectural principles
for energy-efficient management of Clouds; (b) energy-efficient resource
allocation policies and scheduling algorithms considering quality-of-service
expectations, and devices power usage characteristics; and (c) a novel software
technology for energy-efficient management of Clouds. We have validated our
approach by conducting a set of rigorous performance evaluation study using the
CloudSim toolkit. The results demonstrate that Cloud computing model has
immense potential as it offers significant performance gains as regards to
response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference
on Parallel and Distributed Processing Techniques and Applications (PDPTA
2010), Las Vegas, USA, July 12-15, 201
Screening of energy efficient technologies for industrial buildings' retrofit
This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit
Power Management Techniques for Data Centers: A Survey
With growing use of internet and exponential growth in amount of data to be
stored and processed (known as 'big data'), the size of data centers has
greatly increased. This, however, has resulted in significant increase in the
power consumption of the data centers. For this reason, managing power
consumption of data centers has become essential. In this paper, we highlight
the need of achieving energy efficiency in data centers and survey several
recent architectural techniques designed for power management of data centers.
We also present a classification of these techniques based on their
characteristics. This paper aims to provide insights into the techniques for
improving energy efficiency of data centers and encourage the designers to
invent novel solutions for managing the large power dissipation of data
centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy
Efficiency, Green Computing, DVFS, Server Consolidatio
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