Review of big data stream processing: latency optimization and dynamic resource management algorithms

Бойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., Эсонбоев, М.

Рақамли технологияларнинг назарий ва амалий масалалари · 2026-yil

Annotatsiya

This review paper systematically examines modern architectures, algorithms, and approaches aimed at reducing latency and dynamically managing resources in real-time big data stream processing systems. The characteristics of Lambda, Kappa, and hybrid architectures, cloud-native platforms, as well as Edge–Fog–Cloud hybrid environments are analyzed. Network-level and computation-level latency factors, along with mitigation techniques – including operator placement, task offloading, RDMA technology, and machine learning-based prediction models – are investigated. Dynamic resource management issues in cloud environments are addressed, including reactive, proactive, and hybrid auto-scaling algorithms, time series-based approaches, deep learning and reinforcement learning methods, and multi-objective task scheduling. The analysis demonstrates that the highest efficiency is achievable through the integration of complementary approaches – proactive forecasting, hybrid auto-scaling, and ML-based decision-making. Open research challenges and promising future directions are identified.

Maqola ma’lumotlari
MualliflarБойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., Эсонбоев, М.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2026-03-30
Jild9
Son1
Betlar139-153
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v9i1.359
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v9i1.359 · Maqolaning asl sahifasi

Kalit so‘zlar

большие данные, потоковая обработка, Lambda-архитектура, Kappa-архитектура, облачные вычисления, задержка, размещение операторов, автомасштабирование, машинное обучение, динамическое управление ресурсами, Edge–Fog–Cloud, big data, stream processing, Lambda architecture, Kappa architecture, cloud computing, latency, operator placement, auto-scaling, machine learning, dynamic resource management, Edge–Fog–Cloud

Ilmiy soha

Texnika

Iqtibos keltirish

APAБойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., & Эсонбоев, М. (2026). Review of big data stream processing: latency optimization and dynamic resource management algorithms. Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 139-153. https://doi.org/10.62132/ijdt.v9i1.359
GOST (OAK)Бойназаров, И.М. va b. Review of big data stream processing: latency optimization and dynamic resource management algorithms // Рақамли технологияларнинг назарий ва амалий масалалари. – 2026. – T. 9, № 1. – P. 139-153. – DOI: 10.62132/ijdt.v9i1.359.
MLAБойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., and Эсонбоев, М. “Review of big data stream processing: latency optimization and dynamic resource management algorithms.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 139-153, 2026. https://doi.org/10.62132/ijdt.v9i1.359
ChicagoБойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., and Эсонбоев, М. “Review of big data stream processing: latency optimization and dynamic resource management algorithms.” Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 139-153 (2026). https://doi.org/10.62132/ijdt.v9i1.359
HarvardБойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., and Эсонбоев, М (2026) ‘Review of big data stream processing: latency optimization and dynamic resource management algorithms’, Рақамли технологияларнинг назарий ва амалий масалалари, 9(1), 139-153. https://doi.org/10.62132/ijdt.v9i1.359
BibTeX
@article{article2026,
  title = {Review of big data stream processing: latency optimization and dynamic resource management algorithms},
  author = {Бойназаров, И.М. and Маманов, Ж. and Махмудов, Ж.И. and Эсонбоев, М.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2026},
  volume = {9},
  number = {1},
  pages = {139-153},
  doi = {10.62132/ijdt.v9i1.359},
  url = {https://doi.org/10.62132/ijdt.v9i1.359}
}

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