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.
| Mualliflar | Бойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., Эсонбоев, М. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2026-03-30 |
| Jild | 9 |
| Son | 1 |
| Betlar | 139-153 |
| Til | Rus |
| DOI | 10.62132/ijdt.v9i1.359 |
DOI: 10.62132/ijdt.v9i1.359 · Maqolaning asl sahifasi
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
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