Stateless System Performance Prediction and Health Assessment in Cloud Environments: Introducing cSysGuard, an Ensemble Modeling Approach

Abstract

Stateless cloud computing presents remarkable scalability and cost-effectiveness by offering dynamically adjustable resources tailored to fluctuating demands, eliminating the constraints of stateful architectures. However, the challenges presented by dynamic workload are substantial in the context of system health monitoring, frequently leading to service interruptions owing to insufficient resources. It underscores the need for the development of more efficient monitoring systems. Our study introduces cSysGuard, a novel framework designed to enhance monitoring capabilities within cloud environments. The methodology employs an ensemble regression model with a stacking strategy to forecast dynamic performance metrics. The algorithm also leverages a classification model to assess the system’s health based on forecasted metrics, effectively identifying potential failures in the future. Under the configuration utilized, our evaluations demonstrated increased predictive performance with cSysGuard in forecasting various metrics compared to traditional models. The results showed an improvement of up to a remarkable 2.28-fold increase, varying significantly based on the specific metric under consideration. In addition, the effectiveness of health assessment was achieved through Decision Trees with hyperparameter tuning, resulting in a macro-averaged F1 score of 89.79%. This research contributes to both the theoretical and practical aspects of server monitoring, presenting a solution that assesses system performance metrics and health to tackle dynamic challenges in cloud infrastructure.

Keywords

• Cloud computing
• stacking ensemble models
• machine learning
• non-functional testing
• predictive maintenance
• resource allocation