Study on Performance Improvement Based on Meta-Heuristic Algorithms for Efficient Streaming Data Processing 


Vol. 49,  No. 2, pp. 237-245, Feb.  2024
10.7840/kics.2024.49.2.237


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  Abstract

The need to efficiently process and analyze large amounts of streaming data has led to the emergence of various stream processing platforms. However, the challenge remains to optimize several aspects of performance, such as processing speed and usage efficiency. In this study, we introduce a novel approach to address this problem using the Whale Optimization Algorithm (WOA), a metaheuristic algorithm that simulates the hunting behavior of whales. Applied to a stream processing platform, WOA achieves performance improvements compared to existing algorithms and shows parallelism and overall performance gains by tuning the executor settings of distributed computing systems such as Apache Spark where parallelism is important. We also leverage grid search, a hyperparameter optimization technique, to fine-tune the hyperparameters of WOA to achieve additional performance gains.

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[IEEE Style]

D. Kim and Y. Kwon, "Study on Performance Improvement Based on Meta-Heuristic Algorithms for Efficient Streaming Data Processing," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 237-245, 2024. DOI: 10.7840/kics.2024.49.2.237.

[ACM Style]

DaeGwang Kim and Young-Woo Kwon. 2024. Study on Performance Improvement Based on Meta-Heuristic Algorithms for Efficient Streaming Data Processing. The Journal of Korean Institute of Communications and Information Sciences, 49, 2, (2024), 237-245. DOI: 10.7840/kics.2024.49.2.237.

[KICS Style]

DaeGwang Kim and Young-Woo Kwon, "Study on Performance Improvement Based on Meta-Heuristic Algorithms for Efficient Streaming Data Processing," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 237-245, 2. 2024. (https://doi.org/10.7840/kics.2024.49.2.237)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880