| ALOJA: A Framework for Benchmarking and Predictive Analytics in Hadoop Deployments | |
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MSRDG International Journal of Computer Scientific Technology & Electronics Engineering
© 2025 by MSRDG IJCSTEE Journal Volume 1 Issue 5 Year of Publication: 2025 |
Paper Download Article ID MSRDG-IJCSTEE-V1I5P102 |
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Abstract: An approach that uses AI and OCR to digitize and transform ancient, handwritten registered papers into digital representations that are easily accessible. The system seeks to precisely identify and transcribe text from a variety of handwritten sources by combining cutting-edge optical character recognition (OCR) and natural language processing (NLP) techniques. To guarantee widespread accessibility, regional language support is also included. By providing historical records in an organized digital format, this project improves accessibility while addressing preservation-related issues. The suggested solution increases recognition accuracy for different handwriting styles by utilizing character segmentation techniques and deep learning models. Better transcription performance is ensured by the AI model's ability to adjust to handwritten text irregularities through the use of a strong dataset and ongoing training. Reliance on physical records is further decreased by incorporating cloud-based storage solutions, which facilitate effective document. This digitalization strategy improves data security and lifespan in addition to making historical documents easier to retrieve. The system's usability is expanded by its multilingual capability, which enables papers to be translated and transcribed into multiple regional languages. In order to promote knowledge preservation and historical recording, the solution seeks to offer smooth accessibility to scholars, researchers, and the general public through the use of a simple user interface. Furthermore, by transforming ancient registered handwritten documents into a format that is easily readable and accessible, the AI and OCR solution seeks to enhance historical records' readability and public access. The method improves the usefulness of ancient documents by tackling issues including damaged paper, intricate handwriting, and faded ink. Communities with a variety of linguistic backgrounds can benefit from digital records improved to the incorporation of regional language support, which increases the accessibility and inclusivity of historical material. The proliferation of Hadoop-based big data deployments across enterprise and cloud environments has created an acute need for systematic performance characterisation and capacity planning tools. In this paper we present ALOJA, an integrated framework that combines automated benchmarking, fine-grained resource instrumentation, and machine learning-driven predictive analytics for Hadoop clusters. ALOJA automates the execution of representative workloads drawn from the HiBench benchmark suite, captures multi-dimensional performance metrics at subsecond granularity, and uses these observations to train and evaluate a suite of predictive models capable of estimating job execution times before a single task is submitted. Experiments conducted on clusters ranging from 4 to 64 nodes—spanning on-premise bare-metal and three major public cloud providers—demonstrate that ALOJA's Random Forest regressor achieves an R² of 0.923 and a mean absolute error of 54 seconds on held-out test workloads, outperforming linear regression, support vector regression, and gradient boosting baselines. Scalability analysis confirms near-linear speedup for data-parallel workloads up to 32 nodes, beyond which inter-node communication overhead induces measurable divergence from ideal scaling. Resource utilisation heatmaps reveal workload-specific bottleneck patterns, enabling targeted hardware provisioning recommendations. These results establish ALOJA as a practical, production-ready instrumentation and prediction platform for organisations operating large-scale Hadoop environments. |
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| Keywords: Hadoop benchmarking · Predictive analytics · MapReduce · Big data performance · Machine learning · Random Forest regression · YARN resource management Cloud elasticity | |
