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Aman Kansal
Researcher at Microsoft
Publications - 160
Citations - 15209
Aman Kansal is an academic researcher from Microsoft. The author has contributed to research in topics: Wireless sensor network & Data center. The author has an hindex of 54, co-authored 159 publications receiving 14790 citations. Previous affiliations of Aman Kansal include University of California, Los Angeles & Carnegie Mellon University.
Papers
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Journal ArticleDOI
Power management in energy harvesting sensor networks
TL;DR: In this paper, the authors have developed abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues.
Journal Article
Design Considerations for Solar Energy Harvesting Wireless Embedded Systems
TL;DR: In this article, the authors describe key issues and tradeoffs which arise in the design of solar energy harvesting, wireless embedded systems and present the design, implementation, and performance evaluation of Heliomote, their prototype that addresses several of these issues.
Proceedings Article
Energy aware consolidation for cloud computing
TL;DR: The study reveals the energy performance trade-offs for consolidation and shows that optimal operating points exist and the challenges in finding effective solutions to the consolidation problem.
Proceedings ArticleDOI
Q-clouds: managing performance interference effects for QoS-aware clouds
TL;DR: Q-Clouds, a QoS-aware control framework that tunes resource allocations to mitigate performance interference effects, is developed, which uses online feedback to build a multi-input multi-output (MIMO) model that captures performance interference interactions, and uses it to perform closed loop resource management.
Proceedings ArticleDOI
Virtual machine power metering and provisioning
TL;DR: Joulemeter builds power models to infer power consumption from resource usage at runtime and identifies the challenges that arise when applying such models for VM power metering, and shows how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error.