NLP-Powered Text Analytics for Cloud Platforms Intelligent Workload Forecasting and Proactive Autoscaling

Abstract

The high rate of growth of cloud computing services has made effective management of resources inseparable from the maintenance of service performance and, at the same time, reducing the costs of operations. Traditional methods of autoscaling tend to use reactive rules that are based on thresholds, which are not sufficient to respond to the dynamic and unpredictable workload patterns that are seen in the modern cloud settings. To overcome this shortcoming, we introduce a new framework that uses text analytics with natural language processing capabilities to derive workload indicators based on heterogeneous cloud telemetry data, such as logs, metrics, user feedback, and service tickets, to support the intelligent workload prediction and proactive autoscaling. In the suggested methodology, the textual cloud data is transformed to semantically meaningful features, which are then integrated into hybrid forecasting models, which initiate proactive scaling decisions. Experimental analysis of actual cloud traces and model workload conditions shows significant gains in prediction error, resource utilisation and response latency compared to baseline methods.