Optimizing Real-Time Telemetry and Diagnostics with Azure SignalR and Redis Cache Integration
DOI:
https://doi.org/10.5281/zenodo.17226156Keywords:
azure signalr, redis cache, real-time telemetry, diagnostics, cloud computing, scalability, low latency, IoTAbstract
This study looked into how to integrate Azure SignalR and Redis Cache to optimize real-time telemetry and diagnostics. A basic system without Redis integration was contrasted with a prototype system that integrated Redis with Azure SignalR. Metrics such as latency, throughput, scalability, error rates, and cache effectiveness were used to assess performance using simulated telemetry data streams under various client loads. The findings showed that Redis integration enhanced throughput by about 60%, decreased average and peak latency by over 50%, and preserved system stability with up to 10,000 concurrent clients. Redis' dependability for handling frequent queries was confirmed by cache hit ratios that continuously above 90%. The results demonstrated that integrating Redis Cache with Azure SignalR offered a fault-tolerant, low-latency, and scalable solution for real-time telemetry settings. For crucial fields like industrial monitoring, medical diagnostics, and Internet of Things applications needing effective large-scale data processing, this strategy provided significant benefits.
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Copyright (c) 2025 Anup Rao
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Research Articles in 'Applied Science and Engineering Journal for Advanced Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
