CloudInsight: Utilizing a Council of Experts to Predict Future Cloud Application Workloads

Many predictive approaches have been proposed to overcome the limitations of reactive autoscaling on clouds. These approaches leverage workload predictors that are usually targeted for a particular workload pattern and can fail to handle real-world cloud workloads whose patterns may be unknown a priori, may dynamically change over time, or may be irregular. The result is that resources are frequently under-and overprovisioned. To address this problem, we create a novel cloud workload prediction framework called CloudInsight, leveraging the combined power of multiple workload predictors that collectively provide a "council of experts". The weights of the predictors in this ensemble model are determined in real-time based on their accuracy for current workload using multi-class regression. Under real workload traces, CloudInsight has 13% - 27% better accuracy than state-of-the-art predictors. It also has low overhead for predicting future workload changes (<; 100 ms) and creating a new ensemble workload predictor (<; 1.1 sec.).