Рақамли технологияларнинг назарий ва амалий масалалари 9-tom 1-san (2026) · 139-153-betler
Review of big data stream processing: latency optimization and dynamic resource management algorithms
Бойназаров, И.М., Маманов, Ж., Махмудов, Ж.И., Эсонбоев, М.
Annotaciya
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.
большие данныепотоковая обработкаLambda-архитектураKappa-архитектураоблачные вычислениязадержкаразмещение операторовавтомасштабированиемашинное обучениединамическое управление ресурсами
Metadata derekkózi: jurnal OAI-PMH arxivi · Sindex tolıq mátindi saqlamaydı, derekkózge silteme beredi.