Predictive Observability for Scalable Cloud Reliability Engineering

Predictive Observability for Scalable Cloud Reliability Engineering

Praveen Kumar Thota

Cleveland State University, USA

Abstract
Because cloud computing environments support nearly all digital services, they need to be very dependable to maintain smooth delivery. Old-fashioned monitors only detect issues after something fails, which often causes services to be interrupted and downtime to be experienced. This framework is presented to help cloud reliability engineering by making future problems predictable. The way we do this is by reviewing active data, using special models and working with advanced techniques to find out about system problems before end users notice them. It uses flexible designs built for large cloud needs and supports making useful decisions by offering predictive analytics. Several tests have shown that the accuracy of detecting faults is higher and there is less time needed to restore services (MTTR). If we use predictive modeling and observability together, this approach allows cloud infrastructures to become more resilient and can heal themselves which is necessary for next-generation scalable services.

Keywords: Predictive Observability, Cloud Reliability Engineering, Scalable Architecture, Machine Learning, Fault Prediction, Telemetry Data, Self-Healing Systems