Search Results for author: Prashant Shenoy

Found 16 papers, 4 papers with code

LEAD: Towards Learning-Based Equity-Aware Decarbonization in Ridesharing Platforms

no code implementations19 Aug 2024 Mahsa Sahebdel, Ali Zeynali, Noman Bashir, Prashant Shenoy, Mohammad Hajiesmaili

Extensive experiments based on a real-world ride-sharing dataset show that LEAD improves fairness by 2$\times$ when compared to emission-aware ride-assignment and reduces emissions by 70% while ensuring fairness within 66% of the fair baseline.

Fairness

CarbonClipper: Optimal Algorithms for Carbon-Aware Spatiotemporal Workload Management

no code implementations14 Aug 2024 Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant Shenoy

We formalize this as an online problem called spatiotemporal online allocation with deadline constraints ($\mathsf{SOAD}$), in which an online player completes a workload (e. g., a batch compute job) by moving and scheduling the workload across a network subject to a deadline $T$.

Management Scheduling

LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand

no code implementations29 Mar 2024 Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy, Noman Bashir, Prashant Shenoy, Mohammad Hajiesmaili

The task is to dynamically scale resources (e. g., the number of servers) assigned to a job of unknown length such that it is completed before a deadline, with the objective of reducing the carbon emissions of executing the workload.

SODA: Protecting Proprietary Information in On-Device Machine Learning Models

no code implementations22 Dec 2023 Akanksha Atrey, Ritwik Sinha, Saayan Mitra, Prashant Shenoy

The growth of low-end hardware has led to a proliferation of machine learning-based services in edge applications.

Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms

1 code implementation31 Oct 2023 Adam Lechowicz, Nicolas Christianson, Bo Sun, Noman Bashir, Mohammad Hajiesmaili, Adam Wierman, Prashant Shenoy

We introduce competitive (robust) threshold-based algorithms for both the minimization and maximization variants of this problem, and show they are optimal among deterministic online algorithms.

SleepMore: Inferring Sleep Duration at Scale via Multi-Device WiFi Sensing

no code implementations24 Oct 2022 Camellia Zakaria, Gizem Yilmaz, Priyanka Mammen, Michael Chee, Prashant Shenoy, Rajesh Balan

In this paper, we propose SleepMore, an accurate and easy-to-deploy sleep-tracking approach based on machine learning over the user's WiFi network activity.

Uncertainty Quantification

Sustainable Computing -- Without the Hot Air

no code implementations30 Jun 2022 Noman Bashir, David Irwin, Prashant Shenoy, Abel Souza

This growth will translate to exponential growth in computing's energy consumption unless improvements in its energy-efficiency can outpace increases in its demand.

FlowSense: Monitoring Airflow in Building Ventilation Systems Using Audio Sensing

1 code implementation22 Feb 2022 Bhawana Chhaglani, Camellia Zakaria, Adam Lechowicz, Prashant Shenoy, Jeremy Gummeson

With a motivation to monitor airflow from building ventilation systems through commodity sensing devices, we present FlowSense, a machine learning-based algorithm to predict airflow rate from sensed audio data in indoor spaces.

Privacy Preserving speech-recognition +1

WiSleep: Inferring Sleep Duration at Scale Using Passive WiFi Sensing

no code implementations7 Feb 2021 Priyanka Mary Mammen, Camellia Zakaria, Tergel Molom-Ochir, Amee Trivedi, Prashant Shenoy, Rajesh Balan

With a motivation to expand sleep monitoring capabilities at a large scale and contribute sleep data to public health understanding, we present Wisleep, a system for inferring sleep duration using smartphone network connections that are passively sensed from WiFi infrastructure.

Change Point Detection

Preserving Privacy in Personalized Models for Distributed Mobile Services

no code implementations14 Jan 2021 Akanksha Atrey, Prashant Shenoy, David Jensen

We present Pelican, a privacy-preserving personalization system for context-aware mobile services that leverages both device and cloud resources to personalize ML models while minimizing the risk of privacy leakage for users.

Attribute Privacy Preserving

WattScale: A Data-driven Approach for Energy Efficiency Analytics of Buildings at Scale

no code implementations2 Jul 2020 Srinivasan Iyengar, Stephen Lee, David Irwin, Prashant Shenoy, Benjamin Weil

In this paper, we present \texttt{WattScale}, a data-driven approach to identify the least energy-efficient buildings from a large population of buildings in a city or a region.

Bayesian Inference Fault Detection

SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays

no code implementations25 May 2020 Menghong Feng, Noman Bashir, Prashant Shenoy, David Irwin, Beka Kosanovic

There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices.

Anomaly Classification Anomaly Detection +1

WiFiTrace: Network-based Contact Tracing for Infectious Diseases Using Passive WiFi Sensing

1 code implementation25 May 2020 Amee Trivedi, Camellia Zakaria, Rajesh Balan, Prashant Shenoy

Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases.

Networking and Internet Architecture Computers and Society

Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids

no code implementations25 May 2020 Akhil Soman, Amee Trivedi, David Irwin, Beka Kosanovic, Benjamin McDaniel, Prashant Shenoy

Battery-based energy storage has emerged as an enabling technology for a variety of grid energy optimizations, such as peak shaving and cost arbitrage.

Load Forecasting

Empirical Characterization of Mobility of Multi-Device Internet Users

1 code implementation18 Mar 2020 Amee Trivedi, Jeremy Gummeson, Prashant Shenoy

Also, prior work has analyzed mobility at the spatial scale of the underlying mobile dataset and has not analyzed mobility characteristics at different spatial scales and its implications on system design.

Computers and Society

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