• Publications
  • Influence
TUBE: time-dependent pricing for mobile data
TLDR
We present the architecture, implementation, and a user trial of an end-to-end TDP system called TUBE, a price-based feedback control loop between an ISP and its end users that allows users to choose the time and volume of their usage. Expand
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A survey of smart data pricing
TLDR
We review some of the well-known past broadband pricing proposals, including their current realizations in various consumer data plans around the world. Expand
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How to Bid the Cloud
TLDR
Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Expand
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Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility
TLDR
We develop an algorithm for computing day-ahead prices, and another algorithm for estimating and refining user reaction to the prices. Expand
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Multiresource allocation: fairness-efficiency tradeoffs in a unifying framework
TLDR
This paper develops a unifying framework addressing the fairness-efficiency tradeoff in light of multiple types of resources and explores the effect of user requests' heterogeneity. Expand
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AMUSE: Empowering users for cost-aware offloading with throughput-delay tradeoffs
TLDR
We propose AMUSE (Adaptive bandwidth Management through USer-Empowerment), a practical, costaware WiFi offloading system that takes into account a user's throughput-delay tradeoffs. Expand
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Multiresource Allocation: Fairness–Efficiency Tradeoffs in a Unifying Framework
TLDR
This paper develops a unifying framework addressing the fairness-efficiency tradeoff in light of multiple types of resources. Expand
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Sponsoring mobile data: An economic analysis of the impact on users and content providers
TLDR
In this work, we derive the optimal sponsored data behaviors for users, CPs, and ISPs and then consider their implications for heterogeneous CPs and users. Expand
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CYRUS: towards client-defined cloud storage
TLDR
We propose a distributed, client-defined architecture that integrates multiple autonomous CSPs into one unified cloud and allows individual clients to specify their desired performance levels and share files. Expand
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Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks
TLDR
We present a novel method for predicting the evolution of a student's grade in massive open online courses by incorporating another, richer form of data collected from each student—lecture video-watching clickstreams—into the machine learning feature set. Expand
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