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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
Competitive Path Computation and Function Placement in SDNs
Guy Even, Moti Medina, Boaz Patt-Shamir · 2016-02-19 · via cs.DS updates on arXiv.org

We consider a task of serving requests that arrive in an online fashion in Software-Defined Networks (SDNs) with network function virtualization (NFV). Each request specifies an abstract routing and processing "plan" for a flow. Each processing function can be performed by a specified subset of servers in the system. The algorithm needs to either reject the request or admit it and return detailed routing (a.k.a. "path computation") and processing assignment ("function placement"). Each request also specifies the communication bandwidth and the processing load it requires. Components in the system (links and processors) have bounded capacity; a feasible solution may not violate the capacity constraints. Requests have benefits and the goal is to maximize the total benefit of accepted requests. In this paper we first formalize the problem, and propose a new service model that allows us to cope with requests with unknown duration. The new service model augments the traditional accept/reject schemes with a new possible response of "stand by." Our main result is an online algorithm for path computation and function placement that guarantees, in each time step, throughput of at least $Ω\left(\frac{\text{OPT}^*}{\log n}\right)$, where $n$ is the system size and $\text{OPT}^*$ is an upper bound on the maximal possible throughput. The guarantee holds assuming that requests ask for at most an $O\left(1/{\log n}\right)$-fraction of the capacity of any component in the system. Furthermore, the guarantee holds even though our algorithm serves requests in an all-or-nothing fashion using a single path and never preempts accepted flows, while $\text{OPT}^*$ may serve fractional requests, may split the allocation over multiple paths, and may arbitrarily preempt and resume service of requests.