An Approach for Cluster Autoscaling in Cloud Environments based on Workload Prediction

Today, autoscaling in cloud environments is critical and fundamental. This concept refers to a system's ability to adapt to traffic fluctuations and variable user needs without human intervention. Given the rapid changes in demand in the field of information technology, autoscaling serves as a reliable and essential tool, assisting organizations in facing informational market challenges and enhancing system flexibility and efficiency. While existing methods and algorithms have made significant advances, there are deficiencies that require investigation. The complexity and time-consuming nature of algorithms, the need for precise configurations, instability when facing sudden workload spikes, and low accuracy in predicting future workloads are among the issues that necessitate study and research.In this study, an approach for cluster autoscaling in cloud environments based on workload prediction is presented, utilizing a multi-stage hybrid model. This method predicts workload using initial prediction models and enhances the final prediction accuracy and consequently scalability performance through weighting and combining these models. The proposed algorithm in this research has been experimented and evaluated using Google cluster data, showing that it has achieved a precision of 0.99 and an error rate of 0.05 compared to the baseline accuracy of 0.56, indicating an increase in prediction accuracy by 0.34 and better performance with higher accuracy.

An Approach for Cluster Autoscaling in Cloud Environments based on Workload Prediction Keywords:

Autoscaling , Cloud Environment , Machine Learning , Workload Prediction , Ensemble Learning.