Online Weighted Paging with Unknown Weights

Authors: Orin Levy, Noam Touitou, Aviv Rosenberg

NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We present the first algorithm for online weighted paging that does not know page weights in advance, but rather learns from weight samples. In terms of techniques, this requires providing (integral) samples to a fractional solver, requiring a delicate interface between this solver and the randomized rounding scheme; we believe that our work can inspire online algorithms to other problems that involve cost sampling. (From the NeurIPS checklist: This is a theory paper that present an algorithm for the online weighted paging where the weight are unknown and prove the stated bounds.)
Researcher Affiliation Collaboration Orin Levy Tel-Aviv University orinlevy@mail.tau.ac.il Noam Touitou Amazon Science noamtwx@gmail.com Aviv Rosenberg Google Research avivros007@gmail.com
Pseudocode Yes Algorithm 1: Fractional Online Weighted Caching with Unknown Weights, Algorithm 2: Randomized rounding algorithm for OWP-UW, Algorithm 3: Rebalancing procedure for randomized algorithm, Algorithm 4: Optimistic-Pessimistic Weights Estimation Procedure
Open Source Code No The paper is theoretical and does not mention providing open-source code for its methodology.
Open Datasets No The paper is theoretical and does not involve the use of datasets for training or experimentation.
Dataset Splits No The paper is theoretical and does not describe any validation dataset splits as it does not conduct experiments.
Hardware Specification No The paper is theoretical and does not describe any hardware specifications for running experiments.
Software Dependencies No The paper is theoretical and does not list specific software dependencies with version numbers for experimental reproducibility.
Experiment Setup No The paper is theoretical and does not provide details on experimental setup or hyperparameters as it does not conduct experiments.