22 citations · 91 across the 35 of their papers we have counts for
38 papers
Peak-Aware One-Way Trading: Competitive and Learning-Augmented Algorithms for Energy Procurement
Jackson Bibbens, Adam Lechowicz, Nicolas Christianson +2
We introduce peak-aware one-way trading (), an online problem in which a player must purchase a fixed quantity of an asset by a deadline, minimizing its total cost…
Fairness in the k-Server Problem
Mohammadreza Daneshvaramoli, Helia Karisani, Mohammad Hajiesmaili +2
We initiate a formal study of fairness for the -server problem, where the objective is not only to minimize the total movement cost, but also to distribute the cost equitably am…
Quantifying the Carbon Reduction of DAG Workloads: A Job Shop Scheduling Perspective
Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy +2
Carbon-aware schedulers aim to reduce the operational carbon footprint of data centers by running flexible workloads during periods of low carbon intensity. Most schedulers treat w…
A Performance Analyzer for a Public Cloud's ML-Augmented VM Allocator
Roozbeh Bostandoost, Pooria Namyar, Siva Kesava Reddy Kakarla +8
Cloud operators increasingly deploy multiple ML models in their VM allocation pipelines. In such settings, individually benign predictions can shift and compound, severely degradin…
Low-Bitrate Video Compression through Semantic-Conditioned Diffusion
Lingdong Wang, Guan-Ming Su, Divya Kothandaraman +3
Traditional video codecs optimized for pixel fidelity collapse at ultra-low bitrates and produce severe artifacts. This failure arises from a fundamental misalignment between pixel…
Online Smoothed Demand Management
Adam Lechowicz, Nicolas Christianson, Mohammad Hajiesmaili +2
We introduce and study a class of online problems called online smoothed demand management , motivated by paradigm shifts in grid integration and energy storage fo…
Green Bin Packing
Jackson Bibbens, Cooper Sigrist, Bo Sun +2
The online bin packing problem and its variants are regularly used to model server allocation problems. Modern concerns surrounding sustainability and overcommitment in cloud compu…
Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors
Ezra Johnson, Adam Lechowicz, Mohammad Hajiesmaili
A wide range of sustainability and grid-integration strategies depend on workload shifting, which aligns the timing of energy consumption with external signals such as grid curtail…
Smoothed Online Optimization for Target Tracking: Robust and Learning-Augmented Algorithms
Ali Zeynali, Mahsa Sahebdel, Qingsong Liu +2
We introduce the Smoothed Online Optimization for Target Tracking (SOOTT) problem, a new framework that integrates three key objectives in online decision-making under uncertainty:…
Carbon- and Precedence-Aware Scheduling for Data Processing Clusters
Adam Lechowicz, Rohan Shenoy, Noman Bashir +3
As large-scale data processing workloads continue to grow, their carbon footprint raises concerns. Prior research on carbon-aware schedulers has focused on shifting computation to…
Dynamic Incentive Allocation for City-scale Deep Decarbonization
Anupama Sitaraman, Adam Lechowicz, Noman Bashir +3
Greenhouse gas emissions from the residential sector represent a significant fraction of global emissions. Governments and utilities have designed incentives to stimulate the adopt…
Lost in Siting: The Hidden Carbon Cost of Inequitable Residential Solar Installations
Cooper Sigrist, Adam Lechowicz, Jovan Champ +2
The declining cost of solar photovoltaics (PV) combined with strong federal and state-level incentives have resulted in a high number of residential solar PV installations in the U…
Towards Environmentally Equitable AI
Mohammad Hajiesmaili, Shaolei Ren, Ramesh K. Sitaraman +1
The skyrocketing demand for artificial intelligence (AI) has created an enormous appetite for globally deployed power-hungry servers. As a result, the environmental footprint of AI…
Near-Optimal Emission-Aware Online Ride Assignment Algorithm for Peak Demand Hours
Ali Zeynali, Mahsa Sahebdel, Noman Bashir +2
Ridesharing has experienced significant global growth over the past decade and is becoming an integral component of modern transportation systems. However, despite their benefits,…
LEAD: Towards Learning-Based Equity-Aware Decarbonization in Ridesharing Platforms
Mahsa Sahebdel, Ali Zeynali, Noman Bashir +2
Ridesharing platforms such as Uber, Lyft, and DiDi have grown in popularity due to their on-demand availability, ease of use, and commute cost reductions, among other benefits. How…
Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study spatiotemporal online allocation with deadline constraints (), a new online problem motivated by emerging challenges in sustainability and ene…
Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems
Mohammadreza Daneshvaramoli, Helia Karisani, Adam Lechowicz +3
This paper introduces a family of learning-augmented algorithms for online knapsack problems that achieve near Pareto-optimal consistency-robustness trade-offs through a simple com…
LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand
Roozbeh Bostandoost, Adam Lechowicz, Walid A. Hanafy +3
Motivated by an imperative to reduce the carbon emissions of cloud data centers, this paper studies the online carbon-aware resource scaling problem with unknown job lengths (OCSU)…
A Holistic Approach for Equity-aware Carbon Reduction of Ridesharing Platforms
Mahsa Sahebdel, Ali Zeynali, Noman Bashir +2
Ridesharing services have revolutionized personal mobility, offering convenient on-demand transportation anytime. While early proponents of ridesharing suggested that these service…
Robust Learning-Augmented Dictionaries
Ali Zeynali, Shahin Kamali, Mohammad Hajiesmaili
We present the first learning-augmented data structure for implementing dictionaries with optimal consistency and robustness. Our data structure, named RobustSL, is a skip list aug…
Chasing Convex Functions with Long-term Constraints
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study a family of online metric problems with long-term constraints. In these problems, an online player makes decisions in a metric space t…
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study online conversion with switching costs, a family of online problems that capture emerging problems at the intersection of energy and sustainability. In this…
Online Algorithms with Uncertainty-Quantified Predictions
Bo Sun, Jerry Huang, Nicolas Christianson +3
The burgeoning field of algorithms with predictions studies the problem of using possibly imperfect machine learning predictions to improve online algorithm performance. While near…
BONES: Near-Optimal Neural-Enhanced Video Streaming
Lingdong Wang, Simran Singh, Jacob Chakareski +2
Accessing high-quality video content can be challenging due to insufficient and unstable network bandwidth. Recent advances in neural enhancement have shown promising results in im…