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Metric Space Indices for Dynamic Optimization in a Peer to Peer-Based Image Classification Crowdsourcing Platform

Author

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  • Fernando Loor

    (Facultad de Ciencias Físico Matemáticas y Naturales, Universidad Nacional de San Luis, San Luis 5700, Argentina
    These authors contributed equally to this work.)

  • Veronica Gil-Costa

    (Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Facultad de Ciencias Físico Matemáticas y Naturales, Universidad Nacional de San Luis, San Luis 5700, Argentina
    These authors contributed equally to this work.)

  • Mauricio Marin

    (Centre for Biotechnology and Engineering (CeBiB), Departamento de Ingeniería Informática, Universidad de Santiago, Santiago 15782, Chile
    These authors contributed equally to this work.)

Abstract

Large-scale computer platforms that process users’ online requests must be capable of handling unexpected spikes in arrival rates. These platforms, which are composed of distributed components, can be configured with parameters to ensure both the quality of the results obtained for each request and low response times. In this work, we propose a dynamic optimization engine based on metric space indexing to address this problem. The engine is integrated into the platform and periodically monitors performance metrics to determine whether new configuration parameter values need to be computed. Our case study focuses on a P2P platform designed for classifying crowdsourced images related to natural disasters. We evaluate our approach under scenarios with high and low workloads, comparing it against alternative methods based on deep reinforcement learning. The results show that our approach reduces processing time by an average of 40%.

Suggested Citation

  • Fernando Loor & Veronica Gil-Costa & Mauricio Marin, 2024. "Metric Space Indices for Dynamic Optimization in a Peer to Peer-Based Image Classification Crowdsourcing Platform," Future Internet, MDPI, vol. 16(6), pages 1-29, June.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:6:p:202-:d:1410009
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    References listed on IDEAS

    as
    1. Jack P. C. Kleijnen, 2015. "Simulation Optimization," International Series in Operations Research & Management Science, in: Design and Analysis of Simulation Experiments, edition 2, chapter 6, pages 241-300, Springer.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    3. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    4. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
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