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David Irwin
Researcher at University of Massachusetts Amherst
Publications - 161
Citations - 6818
David Irwin is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Cloud computing & Smart grid. The author has an hindex of 36, co-authored 149 publications receiving 5750 citations. Previous affiliations of David Irwin include Duke University.
Papers
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Proceedings ArticleDOI
AutoPlug: An automated metadata service for smart outlets
TL;DR: This work proposes AutoPlug, a system that automatically identifies and tracks the devices plugged into smart outlets in real time without user intervention, and achieves ∼90% identification accuracy on real data collected from 13 distinct device types, while also detecting when a device changes outlets with an accuracy >90%.
Proceedings ArticleDOI
Hedge Your Bets: Optimizing Long-term Cloud Costs by Mixing VM Purchasing Options
TL;DR: This work designs policies to optimize long-term cloud costs by selecting a mix of VM purchasing options based on short- and long- term expectations of workload utilization, and considers a batch trace spanning 4 years from a large shared cluster for a major state University system.
Energy-Agility: A New Grid-centric Metric for Evaluating System Performance
TL;DR: In this article, the authors argue that grid-friendly computer systems are better judged by their energy-agility, rather than energy-eciency, and propose a new grid-centric performance metric, called energy-aggility, that accounts for the assumptions above.
Journal ArticleDOI
Inferring Smart Schedules for Dumb Thermostats
Srinivasan Iyengar,Sandeep Kalra,Anushree Ghosh,David Irwin,Prashant Shenoy,Benjamin M. Marlin +5 more
TL;DR: iProgram addresses new challenges in inferring home occupancy from smart meter data where (i) training data is not available and (ii) the thermostat schedule may be misaligned with occupancy, frequently resulting in high power usage during unoccupied periods.
Proceedings ArticleDOI
SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays
TL;DR: SunDown as mentioned in this paper leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior, can handle concurrent faults in multiple panels, and performs anomaly classification to determine probable causes.