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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
FastReChain: A Novel Bidirectional Model-Based Algorithm ...
Zihan Zhu, Xinchi Han, Dongchao Wu, Zhanbang Zhang, Jian Yang, S · 2025-07-16 · via cs.DS updates on arXiv.org

Optical Circuit Switching (OCS) technology is increasingly being adopted in data centers due to its advantages of low power consumption and low technology refresh costs. Unlike electrical packet switches, OCS provides programmable bandwidth for directly connected devices by configuring the mapping relationships of internal ports. Thus, how to calculate these internal port mapping relationships, i.e., Topology Engineering (ToE), is one of the key designs of OCS-based clusters. Current deployments usually design ToE algorithms by solving Integer Linear Programming (ILP) models, with the aim of minimizing modifications to links occupied by running tasks as much as possible. However, ILP-based ToE algorithms may incur excessive runtime overhead in large-scale clusters. Some existing ToE algorithms convert the ILP model into a Minimum-Cost Flow model through greedy construction, yet such greedy strategies may increase the number of affected links during the OCS reconfiguration process. To solve the aforementioned problems, we propose a novel bidirectional modeling approach, along with a corresponding FastReChain algorithm in this paper. We verify the superiority of this algorithm through simulation experiments based on real-trace data.