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Abstract-This work presents TIRAMOLA, a cloud-enabled, open-source framework to perform automatic resizing of NoSQL clusters according to user-defined policies. Decisions on adding or removing worker VMs from a cluster are modeled as a Markov Decision Process and taken in real-time. The system automatically decides on the most advantageous cluster size according to userdefined policies; it then proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a NoSQL cluster. TIRAMOLA's modular architecture and standard API support allows interaction with most current IaaS platforms and increased customization. An extensive experimental evaluation on an HBase cluster confirms our assertions: The system resizes clusters in real-time and adapts its performance through different optimization strategies, different permissible actions, different input and training loads. Besides the automation of the process, it exhibits a learning feature which allows it to make very close to optimal decisions even with input loads 130% larger or alternating 10 times faster compared to the accumulated information.

The focus of this work is the on-demand resource provisioning in cloud computing, which is commonly referred to as cloud elasticity. Although a lot of effort has been invested in developing systems and mechanisms that enable elasticity, the elasticity decision policies tend to be designed without quantifying or guaranteeing the quality of their operation. We present an approach towards the development of more formalized and dependable elasticity policies. We make two distinct contributions. First, we propose an extensible approach to enforcing elasticity through the dynamic instantiation and online quantitative verification of Markov Decision Processes (MDP) using probabilistic model checking. Second, various concrete elasticity models and elasticity policies are studied. We evaluate the decision policies using traces from a real NoSQL database cluster under constantly evolving external load. We reason about the behaviour of different modeling and elasticity policy options and we show that our proposal can improve upon the state-of-the-art in significantly decreasing under-provisioning while avoiding over-provisioning.

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