CROSS-SYSTEM ANALYSIS OF QUEUEING SYSTEMS INTERACTIONS IN DISTRIBUTED NETWORKS, SUCH AS CLOUD COMPUTING

Аннотация

The increasing reliance on cloud computing has brought both remarkable
opportunities and complex challenges. Distributed networks, such as cloud computing environments,
are inherently dynamic and require robust systems to handle varying workloads
efficiently. This study explores the intricate interactions between multiple queueing models
within these systems, providing a fresh perspective on their impact on performance. By using
queueing theory, we delve into key metrics like response time, system throughput, and
resource allocation strategies. The authors’ analysis highlights the advantages of horizontal
and vertical scaling, container orchestration, and the critical role of network bandwidth and
latency in optimizing cloud infrastructure performance. The researchers also examines how
queueing behaviors affect service-level agreements (SLAs), system availability, fault tolerance,
and energy efficiency. The integration of analytical and simulation-based approaches
enables to evaluate real-world scenarios and identify performance bottlenecks. The findings
not only demonstrate the practical applications of queueing theory but also emphasize its
relevance in managing resource contention and improving elasticity in multi-tenant environments.
This research lays the groundwork for future development of predictive and adaptive
models, contributing to the design of smarter, more scalable, and resilient cloud-based systems.