HADOOP, MULTI-AGENT SYSTEMS AND MACHINE LEARNING: EXPLORING SCALABILITY, FAULT TOLERANCE AND WORKLOAD DISTRIBUTION BEHAVIORS

Bolatzhan Kumalakov; Dilnaz Amangeldi

doi:10.26577/jpcsit4120267

Автор(и)

Bolatzhan Kumalakov Astana IT University, Astana, Kazakhstan https://orcid.org/0000-0003-1476-9542 (неавтентифікований)
Dilnaz Amangeldi Astana IT University, Astana, Kazakhstan https://orcid.org/0009-0008-1908-1556 (неавтентифікований)

DOI:

https://doi.org/10.26577/jpcsit4120267

Ключові слова:

Hadoop, multi-agent systems, Q-Learning, MapReduce programming, distributed computing

Анотація

Paper reports experimental results comparing several machine learning techniques performance when distributing massively parallel computation to a set of interconnected machines. Computational resources are intentionally heterogenous to simulate real ad-hoc network environment and provide realistic setting test results. Namely Round Robin, Q-Learning and Least Loaded algorithms-based solutions are examined for their scalability, fault tolerance and workload distribution behaviors. The novelty of the paper is a set of empirical set of coefficients and bottlenecks for each implementation that is free of infrastructure specifics or error and exemption handling tools for future considerations by engineering professionals and scholars.

Завантажити

Дані для завантаження поки недоступні.

Біографії авторів

автор Bolatzhan Kumalakov, афіліація Astana IT University, Astana, Kazakhstan

Bolatzhan Kumalakov, PhD. Dr. Bolatzhan Kumalakov is an Associate Professor at Astana IT University (Astana, Kazakhstan, bolatzhan.kumalakov@astanait.edu.kz). He received his PhD in Computer Science from al-Farabi Kazakh National University in 2014. Dr. Kumalakov has over 15 years of experience in software engineering, distributed computing, artificial intelligence and machine learning. His research interests include applying machine learning and data mining to solve computational problems in multiple domains. He is a member of the Institute of Electrical and Electronics Engineers (IEEE).
автор Dilnaz Amangeldi, афіліація Astana IT University, Astana, Kazakhstan

Dilnaz Amangeldi is a junior researcher at Astana IT University, School of Artificial Intelligence and Data Science (Astana, Kazakhstan, dilnaz1327@gmail.com). Her academic interests include artificial intelligence, machine learning, and data-driven technologies.