Quantifying the energy consumption of a pocket computer and a Java virtual machine
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Wattch: a framework for architectural-level power analysis and optimizations
Proceedings of the 27th annual international symposium on Computer architecture
Run-time power estimation in high performance microprocessors
ISLPED '01 Proceedings of the 2001 international symposium on Low power electronics and design
Computing in Science and Engineering
Using Complete Machine Simulation for Software Power Estimation: The SoftWatt Approach
HPCA '02 Proceedings of the 8th International Symposium on High-Performance Computer Architecture
An integrated experimental environment for distributed systems and networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
JouleSort: a balanced energy-efficiency benchmark
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Democratizing content publication with coral
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Fine-grained energy profiling for power-aware application design
ACM SIGMETRICS Performance Evaluation Review
A large-scale real-time network simulation study using prime
Winter Simulation Conference
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Reducing the energy consumption of computing systems has been the topic of many research studies. Specifically, optimizing the energy consumption of applications is important to reduce the carbon footprint and the associated costs. In this paper, we detail a method to profile the energy usage of distributed simulations in a grid environment and conduct several experiments towards characterizing the power behavior of our PRIME network simulator. We conclude that although PRIME shows a high level of parallelization in some scenarios, the energy consumption increases significantly as more compute nodes are used to run a network model. Also, we show that cross-traffic has a tremendous impact in the energy usage