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

B
Blog RSS Feed
Martin Fowler
Martin Fowler
爱范儿
爱范儿
IT之家
IT之家
Last Week in AI
Last Week in AI
A
About on SuperTechFans
Google DeepMind News
Google DeepMind News
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
aimingoo的专栏
aimingoo的专栏
G
Google Developers Blog
J
Java Code Geeks
Microsoft Azure Blog
Microsoft Azure Blog
美团技术团队
The Cloudflare Blog
MyScale Blog
MyScale Blog
T
The Blog of Author Tim Ferriss
Hugging Face - Blog
Hugging Face - Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
云风的 BLOG
云风的 BLOG
Y
Y Combinator Blog
The GitHub Blog
The GitHub Blog
腾讯CDC
Microsoft Security Blog
Microsoft Security Blog

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Online Page Migration on Ring Networks in Uniform Model
Amanj Khorramian, Akira Matsubayashi · 2016-12-04 · via cs.IT updates on arXiv.org

This paper explores the problem of page migration in ring networks. A ring network is a connected graph, in which each node is connected with exactly two other nodes. In this problem, one of the nodes in a given network holds a page of size D. This node is called the server and the page is a non-duplicable data in the network. Requests are issued by nodes to access the page one after another. Every time a new request is issued, the server must serve the request and may migrate to another node before the next request arrives. A service costs the distance between the server and the requesting node, and the migration costs the distance of the migration multiplied by D. The problem is to minimize the total costs of services and migrations. We study this problem in uniform model, for which the page has a unit size, i.e. D=1. A 3.326-competitive algorithm improving the current best upper bound is designed. We show that this ratio is tight for our algorithm.