22 citations · 91 across the 35 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting
Adam Lechowicz, Nicolas Christianson, Jinhang Zuo +4
We introduce and study the online pause and resume problem. In this problem, a player attempts to find the lowest (alternatively, highest) prices in a sequence of fixed length…
The Online Knapsack Problem with Departures
Bo Sun, Lin Yang, Mohammad Hajiesmaili +4
The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of di…
Data-driven Competitive Algorithms for Online Knapsack and Set Cover
Ali Zeynali, Bo Sun, Mohammad Hajiesmaili +1
The design of online algorithms has tended to focus on algorithms with worst-case guarantees, e.g., bounds on the competitive ratio. However, it is well-known that such algorithms…
Competitive Algorithms for the Online Multiple Knapsack Problem with Application to Electric Vehicle Charging
Bo Sun, Ali Zeynali, Tongxin Li +3
We introduce and study a general version of the fractional online knapsack problem with multiple knapsacks, heterogeneous constraints on which items can be assigned to which knapsa…
Learning-Assisted Competitive Algorithms for Peak-Aware Energy Scheduling
Russell Lee, Mohammad H. Hajiesmaili, Jian Li
In this paper, we study the peak-aware energy scheduling problem using the competitive framework with machine learning prediction. With the uncertainty of energy demand as the fund…