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

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...

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Bibliographic Details
Published in:Future internet 2024-06, Vol.16 (6), p.202
Main Authors: Loor, Fernando, Gil-Costa, Veronica, Marin, Mauricio
Format: Article
Language:English
Subjects:
Algorithms
Business performance management
Case studies
Classification
Communication
Crowdsourcing
Disasters
Expected values
Germany
Image classification
Metric space
metric spaces
Natural disasters
Optimization
Optimization techniques
P2P–STB-based platform
Parameter estimation
Parameters
performance computing
Performance measurement
Platforms
Queries
self-tuning of parameters
Workloads
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Summary: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%.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi16060202