The public release of Joulemeter is deprecated. This page archives some technologies behind it. The Joulemeter project provides a modeling tool to measure the energy usage of virtual machines (VMs), servers, desktops, laptops, and even individual software applications running on a computer. The visibility provided by Joulemeter is being used to improve power provisioning costs for data centers, virtualized power budgeting, desktop energy optimizations, and mobile battery management.
Note: Joulemeter is no longer available for public download. Similar energy estimation capability is now available through Visual Studio (video).
Energy costs have become increasingly important to computing, since they directly impact the power provisioning cost for computing infrastructures, the operating expense for both data centers and enterprise buildings, as well as battery life for laptops and mobile devices.
The Joulemeter project focuses on the following aspects related to energy optimization:
- Modeling — Joulemeter provides a software tool to estimate the energy usage of a virtual machine (VM), a computer, server, or software application. It also allows modeling the impact of power management of various components such as the CPU, screen, memory, and storage on total power use. Many of these power measurements are not possible in hardware since software components such as VMs do not have a single wire supplying their power where a hardware power meter may be installed.
- Optimization — We use the visibility provided by Joulemeter's modeling techniques to improve power provisioning and consumption costs in various scenarios ranging from data centers, enterprise computing, and battery operated machines.
Short Video: Introducing Joulemeter [3min 30sec]
Detailed Video: Inside Joulemeter [34 min]
Joulemeter estimates the energy usage of a VM, computer, or software by measuring the hardware resources (CPU, disk, memory, screen, etc.) being used and converting the resource usage to actual power usage based on automatically learned realistic power models.
Joulemeter can be used for gaining visibility into energy use and for making several power management and provisioning decisions in data centers, client computing, and software design.
The technology is especially helpful for IT leaders managing power management settings, PC users who wish to get fine grained visibility into their computing energy use, and enthusiast developers who wish to leverage power measurement for optimizing their software and hosted service design for power usage. The fundamental concepts behind how the technology works are available in the paper, Virtual Machine Power Metering and Provisioning.
The visibility provided by the Joulemeter modeling tool can be used to optimize power use in multiple scenarios. The measurement of VM power allows developing power budgeting techniques for virtualized data centers. Managing and tracking PC sleep, combined with remote wakeup, allows optimizing desktop power consumption in enterprise buildings. Separating the impact of hardware components on battery life allows users to trade-off power management settings for improving battery life and enables developers to make appropriate design trade-offs for their software applications. Details on many of these use cases are available in the Joulemeter research publications listed below.
- Radhika Mittal, Aman Kansal, and Ranveer Chandra, Empowering Developers to Estimate App Energy Consumption, in ACM Mobicom, ACM, 26 August 2012.
- Arka Bhattacharya, Aman Kansal, David Culler, Sriram Sankar, and Sriram Govindan, The Need for Speed and Stability in Data Center Power Capping, in Third International Green Computing Conference (IGCC'12), 5 June 2012.
- Harold Lim, Aman Kansal, and Jie Liu, Power Budgeting for Virtualized Data Centers, in 2011 USENIX Annual Technical Conference (USENIX ATC '11), USENIX, 15 June 2011.
- Weiwei Xiong and Aman Kansal, Energy Efficient Data Intensive Distributed Computing, in IEEE Data Engineering (Special Issue on Energy Aware Big Data Processing), IEEE Computer Society, 31 March 2011.
- Christos Kozyrakis, Aman Kansal, Sriram Sankar, and Kushagra Vaid, Server Engineering Insights for Large-Scale Online Services, in IEEE Micro, IEEE, July 2010.
- Joshua Reich, Michel Goraczko, Aman Kansal, and Jitu Padhye, Sleepless In Seattle No Longer, in USENIX Annual Technical Conference, USENIX, 22 June 2010.
- Aman Kansal, Feng Zhao, Jie Liu, Nupur Kothari, and Arka Bhattacharya, Virtual Machine Power Metering and Provisioning , in ACM Symposium on Cloud Computing (SOCC), Association for Computing Machinery, Inc., 10 June 2010.
- Aman Kansal, Jie Liu, Abhishek Singh, Ripal Nathuji, and Tarek Abdelzaher, Semantic-less Coordination of Power Management and Application Performance, in Hotpower 2009 (co-located with SOSP 2009), USENIX, 10 October 2009.
- Shekhar Srikantaiah, Aman Kansal, and Feng Zhao, Energy Aware Consolidation for Cloud Computing, in USENIX HotPower'08: Workshop on Power Aware Computing and Systems at OSDI, USENIX, 7 December 2008.
- Aman Kansal and Feng Zhao, Fine-Grained Energy Profiling for Power-Aware Application Design, in First Workshop on Hot Topics in Measurement and Modeling of Computer Systems (HotMetrics08) at ACM Sigmetrics. (Archived in SIGMETRICS Perform. Eval. Rev.), Association for Computing Machinery, Inc., Annapolis, MD, USA, June 2008.