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Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization

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arXiv 2019年

作者： Paulen, Radoslav Fikar, Miroslav Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Bratislava Slovakia

This paper presents a scheme for dual robust control of batch processes under parametric uncertainty. The dual-control paradigm arises in the context of adaptive control. A trade-off should be decided between the control actions that (robustly) optimize the plant performance and between those that excite the plant such that unknown plant model parameters can be learned precisely enough to increase the robust performance of the plant. Some recently proposed approaches can be used to tackle this problem, however, this will be done at the price of conservativeness or significant computational burden. In order to increase computational efficiency, we propose a scheme that uses parameterized conditions of optimality in the adaptive predictive-control fashion. The dual features of the controller are incorporated through scenario-based (multi-stage) approach, which allows for modeling of the adaptive robust decision problem and for projecting this decision into predictions of the controller. The proposed approach is illustrated on a case study from batch membrane filtration. Copyright © 2019, The Authors. All rights reserved.

关键词： batch data processing

On the Potential of Execution Traces for batch processing Workload Optimization in Public Clouds

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arXiv 2021年

作者： Scheinert, Dominik Alamgiralem, Alireza Bader, Jonathan Will, Jonathan Wittkopp, Thorsten Thamsen, Lauritz Technische Universität Berlin Germany Humboldt-Universität zu Berlin Germany

With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important. Since managing a dedicated infrastructure is in many situations infeasible or uneconomical, users progressively execute their respective workloads in the cloud. As the configuration of workloads and resources is often challenging, various methods have been proposed that either quickly profile towards a good configuration or determine one based on data from previous runs. Still, performance data to train such methods is often lacking and must be costly collected. In this paper, we propose a collaborative approach for sharing anonymized workload execution traces among users, mining them for general patterns, and exploiting clusters of historical workloads for future optimizations. We evaluate our prototype implementation for mining workload execution graphs on a publicly available trace dataset and demonstrate the predictive value of workload clusters determined through traces only. Copyright © 2021, The Authors. All rights reserved.

关键词： batch data processing

Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence

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arXiv 2018年

作者： Brito da Silva, Leonardo Enzo Elnabarawy, Islam Wunsch, Donald C. Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States Applied Computational Intelligence Laboratory Department of Computer Science Missouri University of Science and Technology RollaMO65409 United States CAPES Foundation Ministry of Education of Brazil BrasíliaDF 70040-020 Brazil

This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with the distinctive features of distributed higher-order activation and match functions, using dual vigilance parameters responsible for cluster similarity and data quantization. Together, these allow DDVFA to perform unsupervised modularization, create multi-prototype clustering representations, retrieve arbitrarily-shaped clusters, and control its compactness. Another important contribution is the reduction of order-dependence, an issue that affects any agglomerative clustering method. This paper demonstrates two approaches for mitigating order-dependence: preprocessing using visual assessment of cluster tendency (VAT) or postprocessing using a novel Merge ART module. The former is suitable for batch processing, whereas the latter can be used in online learning. Experimental results in the online learning mode carried out on 30 benchmark data sets show that DDVFA cascaded with Merge ART statistically outperformed the best other ART-based systems when samples were randomly presented. Conversely, they were found to be statistically equivalent in the offline mode when samples were pre-processed using VAT. Remarkably, performance comparisons to non-ART-based clustering algorithms show that DDVFA (which learns incrementally) was also statistically equivalent to the non-incremental (offline) methods of DBSCAN, single linkage hierarchical agglomerative clustering (HAC), and offline version of k-means, while retaining the appealing properties of ART. Links to the source code and data are provided. Considering the algorithm’s simplicity, online learning capability, and performance, it is an ideal choice for many agglomerative clustering applications. Copyright © 2018, The Authors. All rights reserved.

关键词： batch data processing

SmartChoices: Augmenting Software with Learned Implementations

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arXiv 2023年

作者： Golovin, Daniel Bartók, Gábor Chen, Eric Donahue, Emily Huang, Tzu-Kuo Kokiopoulou, Efi Qin, Ruoyan Sarda, Nikhil Sybrandt, Justin Tjeng, Vincent Google DeepMind United Kingdom Google Research Reka AI Google United States

In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall system behavior. While machine learning may outperform these heuristics, replacing existing heuristics in a production system safely and reliably can be prohibitively costly. We present SmartChoices, a novel approach that reduces the cost to deploy production-ready ML solutions for contextual bandits problems. SmartChoices' interface cleanly separates problem formulation from implementation details: engineers describe their use case by defining datatypes for the context, arms, and feedback that are passed to SmartChoices APIs, while SmartChoices manages encoding & logging data and training, evaluating & deploying policies. Our implementation codifies best practices, is efficient enough for use in low-level applications, and provides valuable production features off the shelf via a shared library. Overall, SmartChoices enables non-experts to rapidly deploy production-ready ML solutions by eliminating many sources of technical debt common to ML systems. Engineers have independently used SmartChoices to improve a wide range of software including caches, batch processing workloads, and UI layouts, resulting in better latency, throughput, and click-through rates. © 2023, CC BY.

关键词： batch data processing