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15
No power struggles: Coordinated multi-level power management for the data center
- in ASPLOS
, 2008
"... Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortu ..."
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Cited by 184 (19 self)
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Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately, there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations, workloads, and system design choices.
Self-constructive high-rate system energy modeling for battery-powered mobile systems
- In Proceedings of MobiSys ’11
, 2011
"... System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in a lab setting with the help from a second computer. Not only are they labor-intensive; but also they do not adequately account for the great diversity in t ..."
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Cited by 52 (4 self)
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System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in a lab setting with the help from a second computer. Not only are they labor-intensive; but also they do not adequately account for the great diversity in the hardware and usage of mobile systems. Moreover, existing system energy models are intended for energy estimation for time intervals of one second or longer; they do not provide the required rate for fine-grain use such as per-application energy accounting. In this work, we study a self-modeling paradigm in which a mobile system automatically generates its energy model without any external assistance. Our solution, Sesame, leverages the possibility of self power measurement through the smart battery interface and employs a suite of novel techniques to achieve accuracy and rate much higher than that of the smart battery interface. We report the implementation and evaluation of Sesame on a laptop and a smartphone. The experiment results show that Sesame is able to generate system energy models of 95 % accuracy at one estimation per second and of 88 % accuracy at one estimation per 10 ms, without any external assistance. Two fiveday field studies with four laptop and four smartphones users further demonstrate the effectiveness, efficiency, and noninvasiveness of Sesame.
Feedback control algorithms for power management of servers
- In FeBID
, 2009
"... Power delivery, electricity consumption and heat management are becoming key challenges in data center environments. Solutions have been developed for average and peak power management in the data center. However, these individual solutions are not coordinated resulting in interference and inefficie ..."
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Cited by 13 (0 self)
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Power delivery, electricity consumption and heat management are becoming key challenges in data center environments. Solutions have been developed for average and peak power management in the data center. However, these individual solutions are not coordinated resulting in interference and inefficiency. In this paper, we focus on feedback control algorithms for unified power management of a group of servers through frequency scaling knobs. We present individual efficiency and server capping algorithms, as well as their combined deployment through a unified control architecture. We study the dynamic control algorithms with qualitative and quantitative analysis. The overall results through trace-driven simulations show that the servers under integrated control algorithms achieve good tradeoff among power capping, efficiency and application performance. 1.
Models and Metrics for Energy-Efficient Computer Systems
, 2008
"... Energy efficiency is an important concern in computer systems from small handheld devices to large data centers and supercomputers. Improving energy efficiency requires metrics and models: metrics to assess designs and identify promising energy-efficient technologies, and models to understand the ef ..."
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Cited by 6 (1 self)
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Energy efficiency is an important concern in computer systems from small handheld devices to large data centers and supercomputers. Improving energy efficiency requires metrics and models: metrics to assess designs and identify promising energy-efficient technologies, and models to understand the effects of resource utilization decisions on power consumption. To facilitate energy-efficiency improvements, this dissertation presents Joule-Sort, the first completely specified full-system energy-efficiency benchmark; and Mantis, a generic and portable approach to real-time, full-system power modeling. JouleSort was the first full-system energy-efficiency benchmark with fully specified workload, metric, and rules. This dissertation describes the benchmark design, highlighting the challenges and pitfalls of energy-efficiency benchmarking that distinguish it from benchmarking pure performance. It also describes the design of the machine with the highest known JouleSort score. This machine, consisting of a commodity mobile CPU and 13 laptop drives connected by server-style I/O interfaces, differs greatly from today’s commercially available servers.
Worth their Watts?- An Empirical Study of Datacenter Servers
"... The management of power consumption in datacenters has become an important problem. This needs a systematic evaluation of the as-is scenario to identify potential areas for improvement and quantify the impact of any strategy. We present a measurement study of a production datacen-ter from a joint pe ..."
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Cited by 5 (1 self)
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The management of power consumption in datacenters has become an important problem. This needs a systematic evaluation of the as-is scenario to identify potential areas for improvement and quantify the impact of any strategy. We present a measurement study of a production datacen-ter from a joint perspective of power and performance at the individual server level. Our observations help correlate power consumption of production servers with their activ-ity, and identify easily implementable improvements. We find that production servers are underutilized from an ac-tivity perspective; are overrated from a power perspective; execute temporally similar workloads over a granularity of weeks; do not idle efficiently; and have power consump-tions that are well tracked by their CPU utilizations. Our measurements suggest the following steps for improvement: staggering periodic activities on servers; enabling deeper sleep states; and provisioning based on measurement. 1.
Models and metrics for energy-efficient computing
- Advances in Computers, 75:159–233
, 2009
"... Provided for non-commercial research and educational use only. Not for reproduction, distribution or commercial use. This chapter was originally published in the book Advances in Computers, Vol. 75, published by Elsevier, and the attached copy is provided by Elsevier for the author's benefit an ..."
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Cited by 3 (0 self)
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Provided for non-commercial research and educational use only. Not for reproduction, distribution or commercial use. This chapter was originally published in the book Advances in Computers, Vol. 75, published by Elsevier, and the attached copy is provided by Elsevier for the author's benefit and for the benefit of the author's institution, for non-commercial research and educational use including without limitation use in instruction at your institution, sending it to specific colleagues who know you, and providing a copy to your institution’s administrator. All other uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing copies or access, or posting on open internet sites, your personal or institution’s website or repository, are prohibited. For exceptions, permission may be sought for such use through Elsevier's permissions site at:
Sesame: Self-Constructive Energy Modeling for Battery-Powered Mobile Systems
- in Proceedings of the 9th international conference on Mobile systems, applications, and services (MobiSys'11
, 2011
"... System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in lab with the help from a second computer. Not only are they labor-intensive; but also they will not adequately account for the great diversity in the hardw ..."
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Cited by 1 (0 self)
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System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in lab with the help from a second computer. Not only are they labor-intensive; but also they will not adequately account for the great diversity in the hardware and usage of mobile systems. Moreover, existing system energy models are intended for energy estimation for time intervals of one second or longer; they do not provide the required rate for fine-grain use such as per-application energy accounting. In this work, we study a self-modeling paradigm in which a mobile system automatically generates its energy model without any external assistance. Our solution, Sesame, leverages the possibility of self power measurement through the smart battery interface and employs a suite of novel techniques to achieve accuracy and rate much higher than that of the smart battery interface. We report the implementation and evaluation of Sesame on a laptop and a smartphone. The experiment results show that Sesame generates system energy models of 95% accuracy at one estimation per second and 88 % accuracy at one estimation per 10ms, without any external assistance. A five-day field study with four laptop users further demonstrates the effectiveness, efficiency, and noninvasiveness of Sesame. 1.
Accurate Multicore Processor Power Models for Power-Aware Resource Management
, 2011
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.