惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

量子位
Vercel News
Vercel News
Microsoft Azure Blog
Microsoft Azure Blog
爱范儿
爱范儿
N
Netflix TechBlog - Medium
Google DeepMind News
Google DeepMind News
H
Help Net Security
罗磊的独立博客
The Cloudflare Blog
J
Java Code Geeks
博客园 - 叶小钗
I
InfoQ
B
Blog
Blog — PlanetScale
Blog — PlanetScale
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
腾讯CDC
月光博客
月光博客
博客园_首页
雷峰网
雷峰网
M
MIT News - Artificial intelligence
博客园 - 【当耐特】
美团技术团队
T
The Blog of Author Tim Ferriss
博客园 - 司徒正美

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Online TCP Acknowledgment under General Delays
[Submitted on 15 Apr 2026 (v1), last revised 14 Jul 2026 (this v · 2026-04-15 · via cs.DS updates on arXiv.org

View PDF HTML (experimental)

Abstract:In a seminal work, Dooly, Goldman, and Scott (STOC 1998; JACM 2001) introduced the classic Online TCP Acknowledgment} problem: a sequence of $n$ packets arrives over time, and the objective is to minimize both the number of acknowledgments sent and the total delay experienced by the packets. They showed that a natural greedy algorithm, which acknowledges when the delay of pending packets equals the acknowledgment cost, is $2$-competitive.
Online TCP Acknowledgment is the canonical online problem with delay, capturing the fundamental tradeoff between reducing service cost through batching and the delay incurred by pending requests. Prior work has largely focused on richer service-cost models, e.g., Multi-Level Aggregation. However, besides the work of Albers and Bals (SODA 2003), which studies maximum delay and similar objectives, not much is known beyond the sum of delay costs of requests.
In this work, we study Online TCP Acknowledgment under two generalized delay-cost models. In the batch-aware model, each batch incurs a delay cost that depends on the packet delays within that batch. For the max-over-batches objective, we show that greedy remains $2$-competitive. For the sum-over-batches objective, the picture changes sharply: greedy is $\Omega(n)$-competitive, and the optimal deterministic competitive ratio is $\Theta(\log n)$. Our upper bounds only require the batch delay function to be monotone.
In the batch-oblivious model, the delay cost is a function of the global packet-delay vector. We show that greedy is $2$-competitive for continuous submodular delay costs, and more generally under a weaker zero-coordinate diminishing-marginals condition. This yields $2$-competitive algorithms for ordered norms. Using the submodular-norm approximation of Patton, Russo, and Singla, we also obtain an $O(\log n)$-competitive algorithm for arbitrary symmetric norms.

Submission history

From: Seeun William Umboh [view email]
[v1] Wed, 15 Apr 2026 02:56:51 UTC (30 KB)
[v2] Tue, 14 Jul 2026 05:01:43 UTC (35 KB)