Software–Hardware Cooperative Power Management for Main Memory

Huang, Hai; Shin, Kang G.; Lefurgy, Charles; Rajamani, Karthick; Keller, Teresa; Hensbergen, Eric Van; Rawson, Freeman L.

doi:10.1007/11574859_5

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…Power capping has become a serious challenge for data centers in recent years. Controlling power consumption is an essential way to avoid system failures caused by power capacity overload or overheating due to increasing high server density (e.g., blade servers) [17], [34]. Modern high-density servers face an increasing probability of thermal failures, due to their continuously decreasing size and increasing demand for computational capabilities.…”

Section: Introductionmentioning

confidence: 99%

Exploring power-performance tradeoffs in database systems

Wang

2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

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Abstract-With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11% -22% can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements.

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Section: Introductionmentioning

confidence: 99%

Exploring power-performance tradeoffs in database systems

Wang

2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

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“…High energy consumption is translated to heat dissipation, which in turns increases the cooling cost and could cause the system to failure [16]. The proposed framework assumes 4 states for power modes which are idle, powerdown, power-save and standby modes as shown in Fig 2. The idle power mode is used to stop the MCU only.…”

Section:  Power Managementmentioning

confidence: 99%

A Lightweight Application Framework for Web Enabled Embedded Systems

Sharaf¹,

Hassan²

2013

IJCA

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The spread of embedded systems is growing in very rapidly. Embedded systems are usually suffering from limited resources in terms of processing power, power consumption, memory and storage. These limitations represent a challenge for embedded system developers. In this paper, a lightweight application framework for embedded systems is presented. The application framework provides the developer with powerful components instead of building them from scratch. Using the proposed framework should increase the productivity and flexibility. It should also minimize the effort and time. The proposed framework includes power management, memory management, event driven mechanism, timer management and TCP/IP compact stack.

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“…Therefore, we consider more aggressive optimizations that offer energy savings even for the currently active rank. Immediate Power Down (IPD) mechanism and Immediate Self Refresh (ISR) [15] mechanism have been proposed for RAM to provide on-demand power state transitions and improve energy efficiency of active ranks.…”

Section: B On-demand Mechanismsmentioning

confidence: 99%

Exploring memory energy optimizations in smartphones

Duan

Gniady

2011

2011 International Green Computing Conference and Workshops

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Abstract-Recent development of sophisticated smartphones has made them indispensable part of our everyday life. However, advances in battery technology cannot keep up with the demand for longer battery life. Subsequently, energy efficiency has become one of the most important factors in designing smartphones. Multitasking and better multimedia features in the mobile applications continuously push memory requirements further, making energy optimizations for memory critical. Mobile RAM is already optimized for energy efficiency at the hardware level. It also provides power state switching interfaces to the operating system which enables the OS level energy optimizations. Many RAM optimizations have been explored for computer systems and in this paper we explore their applicability to smartphone hardware. In addition, we apply those optimizations to the newly emerging Phase Change Memory and study their energy efficiency and performance. Finally, we propose a hybrid approach to take the advantage of both Mobile RAM and Phase Change Memory. Results show that our hybrid mechanism can save more than 98% of memory energy as compared to the standard smartphone system with negligible impact on user experience.

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Software–Hardware Cooperative Power Management for Main Memory

Cited by 13 publications

References 11 publications

Exploring power-performance tradeoffs in database systems

Exploring power-performance tradeoffs in database systems

A Lightweight Application Framework for Web Enabled Embedded Systems

Exploring memory energy optimizations in smartphones

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