Abstract

The ever-increasing utilization of cloud applications and services is placing ever-increasing strain on network latency, making end-to-end (E2E) delay a critical performance consideration in cloud internet infrastructures. In this paper, the end-to-end (E2E) delay is analyzed in detail, with a complete decomposition of its constituent delay components: processing delay, queuing Delay, transmission delay, and propagation delay, about the cloud communication stack. Both cloud service interaction analytic models and measurement models are used to study delay across workloads and routing configurations. The study incorporates performance evaluations from public cloud providers, as well as hybrid deployments, and streaming service performance from data centers worldwide. Powerful simulation programs and techniques for measuring latency are used to relate system events to network delays, elucidating delay bottlenecks and mitigation strategies. In addition, neural network-based regression models are trained on traffic conditions, bandwidth, and packet data to forecast delays, estimating worst-case scenarios and informing traffic management strategies. The overarching end-to-end (E2E) delays caused by virtualization overhead, TCP retransmissions, and multi-hop network traversal are highlighted. The results are crucial for services that aim to optimize quality of service policies, traffic management, and dynamic routing adjustments. This study also outlines a systematic approach for designing applications that considers delays as an essential aspect for real-time analytics, telemedicine, cloud gaming, and other latency-sensitive services.