A fuzzy-based auto-scaler for web applications in cloud computing environments

Cloud computing provided the elasticity for its users allowing them to add or remove virtual machines depending on the load of their web applications. However, there is still no ideal auto-scaler which is both easy to use and sufficiently accurate to make web applications resilient under the dynamic load. The threshold-based auto-scaling approaches are among the most popular reactive auto-scaling strategies due to their high learnability and usability. However, the static threshold would become undesirable once the workload becomes highly dynamic and unpredictable. In this paper, we propose a novel fuzzy logic based approach that automatically and adaptively adjusts thresholds and cluster size for a web application. The proposed auto-scaler aims at reducing resource consumption without violation of Service Level Agreement (SLA). The performance evaluation is conducted with the real-life Wikipedia traces in the Amazon Web Services cloud platform. Experimental results demonstrate that our reactive auto-scaler efficiently reduces cloud resources usage and minimizes the SLA violations.