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

Yash Madhwal; Arseny Bolotnikov; Mark Prikhno; Irina Lebedeva; Ivan Laishevskiy; Vladimir Gorgadze; Artem Barger; Yury Yanovich

cs.DCMay 4, 2026

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

Authors: Yash Madhwal, Arseny Bolotnikov, Mark Prikhno, Irina Lebedeva, Ivan Laishevskiy, Vladimir Gorgadze, Artem Barger, Yury Yanovich

Organizations: Skolkovo Institute of Science and Technology, Moscow, Russia · HSE University, Moscow, Russia · Sberbank PJSC, Moscow, Russia · Mid Hope Technologies, Moscow, Russia · Moscow Institute of Physics and Technology, Moscow, Russia · IDEAS: Inter-Disciplinary & Advanced Studies Center, Moscow, Russia · University of Jyväskylä, Jyväskylä, Finland

Abstract

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.

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Aug 13, 2026 · cs.LGJ/K move · Enter open · S save

Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization

Fin Amin, Sounak Dutta, Paul D. Franzon

Bayesian optimization (BO) is a sample-efficient framework for analog circuit topology search, where evaluating each candidate topology can require costly simulation. However, representation-based BO methods typically treat circuit embeddings as fixed after encoder training. This creates a mismatch between representation learning and optimization: embeddings learned to encode or reconstruct circuit structure are not necessarily organized according to the figure of merit (FoM) being optimized. This paper introduces Test-Time Analog Representation Adaptation for Bayesian Optimization (TTARO), an online deep-kernel BO framework that adapts circuit representations throughout the search process. Starting from pretrained circuit embeddings, TTARO jointly learns a nonlinear feature transformation and a Gaussian-process surrogate using the FoM labels of the circuits evaluated so far. Following each new evaluation, TTARO updates the representation and surrogate before selecting the next candidate. We compare TTARO with conventional Gaussian Process-based BO over fixed embeddings and with Deep Kernel Learning (DKL), which learns the representation only from the initial evaluated designs and keeps it fixed throughout the remainder of the search. By continually incorporating newly observed FoM labels into representation learning, TTARO aligns the search space with the optimization objective as BO progresses. In our experiments, TTARO reduces regret AUC by 15.2% on average relative to BO and by 20.7% relative to DKL across 40 encoder/kernel/acquisition settings, outperforming prior art in most settings with reductions as large as 46.7%.

Bayesian Optimization Analog Circuits

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Abstract

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