Distortion-Oblivious Algorithms for Minimizing Flow Time
Not on the map. Why?
Abstract (arXiv)
We consider the classic online problem of scheduling on a single machine to minimize total flow time. In STOC 2021, the concept of robustness to distortion in processing times was introduced: for every distortion factor μ, an O(μ2)-competitive algorithm operatornameALGμ which handles distortions up to μ was presented. However, using that result requires one to know the distortion of the input in advance, which is impractical. We present the first distortion-oblivious algorithms: algorithms which are competitive for every input of every distortion, and thus do not require knowledge of the distortion in advance. Moreover, the competitive ratios of our algorithms are tildeO(μ), which is a quadratic improvement over the algorithm from STOC 2021, and is nearly optimal (we show a randomized lower bound of Omega(μ) on competitiveness).
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Sep 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 4; 2023: 4; 2024: 3; 2025: 2; 2026: 2.
15 papers.
Sort:
By area:
- 2022Permutation Predictions for Non-Clairvoyant Schedulingcited by 22
- 2022Algorithms with Prediction Portfolioscited by 19
- 2025Online Scheduling for LLM Inference with KV Cache Constraintscited by 17
- 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problemscited by 14
- 2023Sorting with Predictionscited by 14
- 2024Algorithms for Caching and MTS with reduced number of predictionscited by 12
- 2023Minimalistic Predictions to Schedule Jobs with Online Precedence Constraintscited by 7
- 2022Scheduling with Speed Predictionscited by 6
- 2023Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessarycited by 5
- 2025Queueing, Predictions, and LLMs: Challenges and Open Problemscited by 4
- 2024SkipPredict: When to Invest in Predictions for Schedulingcited by 3
- 2024Learning-Based Heavy Hitters and Flow Frequency Estimation in Streamscited by 3
- 2026Robust Non-Clairvoyant Scheduling with Classification Models1 Oct 2026
- 2026Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications4 Sep 2026
- 2025Queueing, Predictions, and LLMs: Challenges and Open Problems10 Mar 2025
- 2025Online Scheduling for LLM Inference with KV Cache Constraints10 Feb 2025
- 2024Learning-Based Heavy Hitters and Flow Frequency Estimation in Streams24 Jun 2024
- 2024Algorithms for Caching and MTS with reduced number of predictions9 Apr 2024
- 2024SkipPredict: When to Invest in Predictions for Scheduling5 Feb 2024
- 2023Sorting with Predictions1 Nov 2023
- 2023Sorting and Hypergraph Orientation under Uncertainty with Predictions16 May 2023
- 2023Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary2 Feb 2023
- 2023Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints30 Jan 2023
- 2022Algorithms with Prediction Portfolios22 Oct 2022
3 more
- 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problems25 May 2022
- 2022Scheduling with Speed Predictions2 May 2022
- 2022Permutation Predictions for Non-Clairvoyant Scheduling21 Feb 2022
- 2022Permutation Predictions for Non-Clairvoyant Schedulingcited by 22
- 2022Algorithms with Prediction Portfolioscited by 19
- 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problemscited by 14
- 2023Sorting with Predictionscited by 14
- 2024Algorithms for Caching and MTS with reduced number of predictionscited by 12
- 2023Minimalistic Predictions to Schedule Jobs with Online Precedence Constraintscited by 7
- 2022Scheduling with Speed Predictionscited by 6
- 2023Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessarycited by 5
- 2025Queueing, Predictions, and LLMs: Challenges and Open Problemscited by 4
- 2024SkipPredict: When to Invest in Predictions for Schedulingcited by 3
- 2024Learning-Based Heavy Hitters and Flow Frequency Estimation in Streamscited by 3
- 2023Sorting and Hypergraph Orientation under Uncertainty with Predictionscited by 2
12 most cited of 14 All 14 (JSON)
- 2026Robust Non-Clairvoyant Scheduling with Classification Models1 Oct 2026
- 2026Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications4 Sep 2026
- 2025Queueing, Predictions, and LLMs: Challenges and Open Problems10 Mar 2025
- 2024Learning-Based Heavy Hitters and Flow Frequency Estimation in Streams24 Jun 2024
- 2024Algorithms for Caching and MTS with reduced number of predictions9 Apr 2024
- 2024SkipPredict: When to Invest in Predictions for Scheduling5 Feb 2024
- 2023Sorting with Predictions1 Nov 2023
- 2023Sorting and Hypergraph Orientation under Uncertainty with Predictions16 May 2023
- 2023Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary2 Feb 2023
- 2023Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints30 Jan 2023
- 2022Algorithms with Prediction Portfolios22 Oct 2022
- 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problems25 May 2022
12 newest of 14 All 14 (JSON)