Optimizing Distributed Computing Architectures for Scalable Big Data Analytics
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P102Keywords:
Distributed Computing, Big Data Analytics, Apache Hadoop, Apache Spark, Load Balancing, Task Scheduling Algorithms, Data Locality Optimization, Resource Allocation, Data Security and Privacy, Cloud ComputingAbstract
The advent of big data has transformed how organizations operate, demanding efficient analytics solutions capable of processing vast volumes of data from diverse sources. Distributed computing architectures have emerged as a fundamental approach to tackling these challenges. This paper explores methodologies for optimizing these architectures, presenting various strategies and algorithms designed to enhance scalability and performance in big data analytics applications. By analyzing key frameworks such as Apache Hadoop and Apache Spark, we highlight best practices and future directions for research in optimizing distributed computing for big data analytics
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References
[1] Pennylane. What is quantum machine learning?. https://pennylane.ai/qml/whatisqml
[2] DZone. Quantum ML for large-scale data-intensive applications. https://dzone.com/articles/quantum-ml-for-large-scale-dataintensive-apps
[3] EditVerse. Quantum machine learning: When AI meets quantum computing. https://editverse.com/quantum-machine-learningwhen-ai-meets-quantum computing/
[4] MDPI. (2023). Entropy in quantum machine learning models. Entropy, 25(2), 287. https://www.mdpi.com/1099-4300/25/2/287
[5] Nature. (2017). Supervised learning with quantum-enhanced feature spaces. Nature, 534, 195–202. https://www.nature.com/articles/nature23474
[6] Wikipedia. Quantum machine learning. https://en.wikipedia.org/wiki/Quantum_machine_learning
[7] University of Diyala. Quantum computing and machine learning applications (Book 5). https://sciences.uodiyala.edu.iq/uploads/computer%20sciences/%D9%83%D8%AA%D8%A7%D8%A8%205.pdf
