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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
Systematic Testing of Multicast Routing Protocols: Analys...
Ahmed Helmy, Deborah Estrin, Sandeep Gupta · 2000-07-05 · via cs.DS updates on arXiv.org

In this paper, we present a new methodology for developing systematic and automatic test generation algorithms for multipoint protocols. These algorithms attempt to synthesize network topologies and sequences of events that stress the protocol's correctness or performance. This problem can be viewed as a domain-specific search problem that suffers from the state space explosion problem. One goal of this work is to circumvent the state space explosion problem utilizing knowledge of network and fault modeling, and multipoint protocols. The two approaches investigated in this study are based on forward and backward search techniques. We use an extended finite state machine (FSM) model of the protocol. The first algorithm uses forward search to perform reduced reachability analysis. Using domain-specific information for multicast routing over LANs, the algorithm complexity is reduced from exponential to polynomial in the number of routers. This approach, however, does not fully automate topology synthesis. The second algorithm, the fault-oriented test generation, uses backward search for topology synthesis and uses backtracking to generate event sequences instead of searching forward from initial states. Using these algorithms, we have conducted studies for correctness of the multicast routing protocol PIM. We propose to extend these algorithms to study end-to-end multipoint protocols using a virtual LAN that represents delays of the underlying multicast distribution tree.