Cross Node Telemetry for CPU Efficient Congestion Monitoring

Authors: Arunkumar Sambandam

Country: United States

Full-text Research PDF File:   View   |   Download

Abstract: Network congestion monitoring has become increasingly important in modern distributed and cloud based infrastructures where numerous nodes exchange large volumes of traffic simultaneously. To maintain reliability and service quality, systems continuously collect telemetry information such as bandwidth usage, packet loss, queue occupancy, and delay statistics. Conventional monitoring frameworks analyze these metrics independently at each node and forward the processed information to centralized controllers or logging services. Although this design provides basic visibility, it introduces substantial computational overhead due to repetitive data collection, duplicate analysis, and frequent synchronization across nodes. Each node performs similar monitoring tasks without considering shared or correlated network behavior, resulting in inefficient use of processing resources. As the number of nodes grows, the volume of telemetry data increases proportionally, leading to excessive processing and continuous diagnostic operations Delayed identification of bottlenecks often triggers repeated diagnostics and unnecessary reprocessing of telemetry streams, further amplifying processor load. Consequently, systems experience persistently high CPU utilization, increased latency, and reduced throughput even when the actual network traffic does not justify such resource consumption. Furthermore, centralized aggregation and frequent communication among monitoring components introduce additional overhead and scalability challenges. Adding more nodes or increasing monitoring frequency typically worsens processor utilization rather than improving efficiency. This imbalance between monitoring cost and operational benefit limits the effectiveness of existing solutions and restricts overall system scalability. High CPU consumption dedicated to monitoring tasks ultimately impacts responsiveness, service stability, and resource allocation in distributed environments. This paper addresses the problem of excessive CPU utilization in network congestion monitoring and focuses on improving processor efficiency to support scalable and resource efficient operation across distributed systems.

Keywords: Telemetry, Congestion, Monitoring, Correlation, Distributed, Networks, Scalability, Utilization, Overhead, Diagnostics, Synchronization, Performance, Efficiency, Latency, Throughput.

Paper Id: 232943

Published On: 2023-01-06

Published In: Volume 11, Issue 1, January-February 2023

Share this