Workload and Resource Aware Proactive Auto-scaler for PaaS Cloud

Elasticity is a key feature in Cloud Computing where virtualized resources are provisioned and de-provisioned via auto-scaling. However, auto-scaling in most Platform-as-a-Service (PaaS) systems is based on reactive, threshold-driven approaches. Such systems are incapable of catering to rapidly varying workloads, unless the associated thresholds are sufficiently low. Alternatively, maintaining low thresholds leads to resource over-provisioning under relatively stable workloads. Moreover, thresholds are not a good indication of QoS compliance, which is a key performance indicator of a cloud application. Hence, it is nontrivial to determine an optimum threshold while minimizing costs and meeting QoS demands. We propose inteliScaler, a proactive and cost-aware auto-scaling solution to address these issues by combining a predictive model, cost model, and a smart killing feature. An ensemble workload prediction mechanism is introduced based on time series and machine learning techniques for making accurate predictions on drastically different workload patterns. Utility of the solution is demonstrated using both simulations and empirical evaluations using Apache Stratos PaaS (deployed on the AWS EC2), as well as RUBiS and real-world workload traces. Results show significant QoS improvements and cost reductions by inteliScaler compared to a typical reactive and threshold-based PaaS auto-scaling solution.