Automated workload-aware elasticity of NoSQL clusters in the cloud

The use of cloud computing has gained extreme popularity. Through cloud platforms that provide infrastructure as a service (IaaS), users can elastically provision resources enabling automated application throttling. Usually, scaling is either manually performed or through a service that dynamically consolidates cloud resources based on a predefined policy. However, these policies are simplistic, threshold based and may not be able to capture specific application behaviors according to configuration parameters and applied workload type. In this work, we extend TIRAMOLA, a cloud-enabled framework that allows automated resizing of NoSQL clusters, in order to identify different workload types and apply the most beneficial scaling action according to user defined policies. We perform a thorough analysis of how different query types are handled by modern NoSQL systems and evaluate the performance of a NoSQL cluster of varying size, over mixed workload types and magnitudes. We utilize this knowledge to fine tune the extended TIRAMOLA´s policies in order to take accurate scaling decisions. We perform an extensive experimental evaluation of workload aware and unaware versions on an HBase cluster and our analysis confirms that the former can operate successfully in any environment, behaving accordingly to any input load.