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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
Congestion Control in the Internet by Employing a Ratio d...
Shahram Jamali, Morteza Analoui · 2009-10-12 · via cs.DS updates on arXiv.org

The demand for Internet based services has exploded over the last decade. Many organizations use the Internet and particularly the World Wide Web as their primary medium for communication and business. This phenomenal growth has dramatically increased the performance requirements for the Internet. To have a high performance Internet, a good congestion control system is essential for it. The current work proposes that the congestion control in the Internet can be inspired from the population control tactics of the nature. Toward this idea, each flow (W) in the network is viewed as a species whose population size is congestion window size of the flow. By this assumption, congestion control problem is redefined as population control of flow species. This paper defines a three trophic food chain analogy in congestion control area, and gives a ratio dependent model to control population size of W species within this plant herbivore carnivorous food chain. Simulation results show that this model achieves fair bandwidth allocation, high utilization and small queue size. It does not maintain any per flow state in routers and have few computational loads per packet, which makes it scalable.