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Artem Barger

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Artificial Intelligence Data Structures and Algorithms Distributed, Parallel, and Cluster Computing

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preprint2026arXiv

Caliper-in-the-Loop: Black-Box Optimization for Hyperledger Fabric Performance Tuning

Hyperledger Fabric performance depends on many interacting configuration parameters, making manual tuning difficult. We study automated throughput tuning by treating benchmarking as a noisy black-box optimization problem and applying Bayesian optimization (BO) with dimensionality reduction (DR). We implement an end-to-end Caliper-in-the-loop pipeline that deploys candidate configurations, benchmarks them, and updates the optimizer from observed throughput. The search space, derived from Fabric configuration files, has 317 dimensions. In a cloud testbed, we evaluate 16 BO+DR variants and a random-search baseline. The best method, DYCORS-PCA, achieves a 12% TPS improvement relative to the first evaluated configuration, while MPI-REMBO achieves 9%. These results suggest that BO with DR is a practical approach for high-dimensional Hyperledger Fabric tuning, while also highlighting the role of measurement noise in interpreting gains.

preprint2016arXiv

k-Means for Streaming and Distributed Big Sparse Data

We provide the first streaming algorithm for computing a provable approximation to the $k$ -means of sparse Big data. Here, sparse Big Data is a set of $n$ vectors in $\mathbb{R}^d$ , where each vector has $O(1)$ non-zeroes entries, and $d\geq n$ . E.g., adjacency matrix of a graph, web-links, social network, document-terms, or image-features matrices. Our streaming algorithm stores at most $\log n\cdot k^{O(1)}$ input points in memory. If the stream is distributed among $M$ machines, the running time reduces by a factor of $M$ , while communicating a total of $M\cdot k^{O(1)}$ (sparse) input points between the machines. % Our main technical result is a deterministic algorithm for computing a sparse $(k,ε)$ -coreset, which is a weighted subset of $k^{O(1)}$ input points that approximates the sum of squared distances from the $n$ input points to every $k$ centers, up to $(1\pmε)$ factor, for any given constant $ε>0$ . This is the first such coreset of size independent of both $d$ and $n$ . Existing algorithms use coresets of size at least polynomial in $d$ , or project the input points on a subspace which diminishes their sparsity, thus require memory and communication $Ω(d)=Ω(n)$ even for $k=2$ . Experimental results real public datasets shows that our algorithm boost the performance of such given heuristics even in the off-line setting. Open code is provided for reproducibility.

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