Energy Efficient Computing
Energy%20efficient%20enterprise%20computing
Energy is the limiting resource in a huge range of computing systems, from embedded sensors to mobile phones to data centers. We research how to design and build computer systems to manage energy and minimize its consumption. This work includes wireless sensing deployments to measure where energy goes in building-scale computer system, software techniques to track energy at microjoule accuracy on embedded systems, and operating systems for phones that make energy a first-class abstraction. Computing systems account for at least 13% of the electricity use of office buildings. This translates to about 2% of the electricity consumption of the entire US or the equivalent of the State of New Jersey! As computing becomes pervasive, making these systems more efficient is an opportunity to reduce operational costs and have a positive environmental impact. Unfortunately, current understanding of energy consumption in office buildings is too limited and coarse-grained. Without better visibility into how electricity is spent and how much of it is wasted, it is difficult to find ways to reduce it. Powernet - a multi-year power and utilization study of the computing infrastructure in the Computer Science Department at Stanford University - begins to address the visibility problem in one building. Powernet's data is collected via a large network of plug-level wireless power meters and software sensor that cover a significant portion of the 2nd, 3rd, and 4th floors of the Gates building at Stanford. We use the data insights from Powernet to propose a novel system architecture for office computing, Anyware. To save energy, Anyware leverages two observations. First, higher power draw does not translate to proportionally higher performance. Second, there is a range of resources one can have for a fixed power budget. Anyware's hybrid design splits workload execution between a local low-power client device and a virtual machine (VM) on a backend server. Applications that benefit from hardware optimizations, such as video and graphics, remain local; other tasks (document and picture editing, PDF viewing, etc.) are offloaded to the server. Cinder is a new operating system designed to make resource allocation, accounting, subdivision, and delegation explicit, making the system ideal for resource constrained systems like mobile devices and cellular phones. Cinder is based on HiStar. The Cinder kernel runs on both amd64 and ARM architectures. Our current prototype mobile platform is the HTC Dream cellular phone (the Google G1), which presently supports basic functionality including mobile data access.

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Publications

Multiprogramming a 64 kB Computer Safely and Efficiently.

Amit Levy and Bradford Campbell and Branden Ghena and Daniel Giffin and Pat Pannuto and Prabal Dutta and Philip Levis. In Proceedings of the The 26th ACM Symposium on Operating Systems Principles (SOSP 2017), 2017.

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The Case for Writing a Kernel in Rust.

Amit Levy, Bradford Campbell, Branden Ghena, Pat Pannuto, Prabal Dutta, Philip Levis. In Proceedings of the Eighth ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2017), 2017.

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Beetle: Flexible Communication for Bluetooth Low Energy.

Amit Levy, James Hong, Laurynas Riliskis, Philip Levis, and Keith Winstein. In Proceedings of the 14th International Conference on Mobile Systems, Applications and Services (MobiSys), 2016.

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CESEL: Securing a Mote for 20 Years.

Kevin Kiningham, Mark Horowitz, Philip Levis, and Dan Boneh. In Proceedings of the 13th European conference on Wireless sensor networks (EWSN 2016), 2016.

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Ravel: Programming IoT Applications as Distributed Models, Views, and Controllers.

Laurynas Riliskis, James Hong, and Philip Levis. In Proceedings of the he 2015 International Workshop on Internet of Things towards Applications (IoT-App'15), 2015.

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Ownership is Theft: Experiences Building an Embedded OS in Rust.

Amit Levy, Michael P Andersen, Bradford Campbell, David Culler, Prabal Dutta, Branden Ghena, Philip Levis and Pat Pannuto. In Proceedings of the 8th Workshop on Programming Languages and Operating Systems (PLOS 2015), 2015.

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System Architecture Support for Green Enterprise Computing.

Maria Kazandjieva, Chinmayee Shah, Ewen Cheslack-Postava, Behram Mistree, Philip Levis. In Proceedings 5th International Green Computing Conference (IGCC), 2014.

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Measuring and Analyzing the Energy Use of Enterprise Computing Systems.

Maria Kazandjieva, Omprakash Gnawali, Philip Levis, and Christos Kozyrakis. In Journal of Sustainable Couputing, 2013.

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Green Enterprise Computing Data: Assumptions and Realities.

Maria Kazandjieva, Brandon Heller, Omprakash Gnawali, Philip Levis, and Christos Kozyrakis. In Proceedings of the Third International Green Computing Conference (IGCC), 2012.

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Energy Management in Mobile Devices with the Cinder Operating System.

Arjun Roy, Stephen Rumble, Ryan Stutsman, Philip Levis, David Mazieres, and Nickolai Zeldovich. In Proceedings of the European Conference on Computer Systems (EuroSys 2011), 2011.

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Energy Dumpster Diving.

Maria Kazandjieva, Brandon Heller, Philip Levis, and Christos Kozyrakis. In Second Workshop on Power Aware Computing (HotPower), 2009.

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Apprehending Joule Thieves with Cinder.

Stephen Rumble, Ryan Stutsman, Philip Levis, David Mazieres, and Nickolai Zeldovich. In Proceedings of the First ACM SIGCOMM Workshop on Networking, Systems, Applications on Mobile Handhelds (MobiHeld), 2009.

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Quanto: Tracking Energy in Networked Embedded Systems.

Rodrigo Fonseca, Prabal Dutta, Philip Levis, and Ion Stoica. In Proceedings of the Eighth USENIX Symposium on Operating System Design and Implementation (OSDI), 2008.

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Integrating Concurrency Control and Energy Management in Device Drivers.

Kevin Klues, Vlado Handziski, Chenyang Lu, Adam Wolisz, David Culler, David Gay, and Philip Levis. In Proceedings of the 21st ACM Symposium on Operating System Principles (SOSP), 2007.

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Policies for Dynamic Clock Scheduling.

Dirk Grunwald, Philip Levis, Charles B. Morrey III, and Michael Neufeld. In Proceedings of the 4th Symposium on Operating System Design and Implementation (OSDI), 2000.

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Funding
This work was supported by the Department of Energy ARPA-E program under award number DE-AR0000018, generous gifts from DoCoMo Capital, the National Science Foundation under grants #0832820, #0831163, #0846014 and #0546630, the King Abdullah University of Science and Technology (KAUST), Microsoft Research, scholarships from the Samsung Scholarship Foundation, a Stanford Graduate Fellowship and a Stanford Terman Fellowship.






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