Transactional Auto Scaler: Elastic scaling of NoSQL transactional data grids
نویسندگان
چکیده
In this paper we introduce TAS (Transactional Auto Scaler), a system that relies on a novel hybrid analytical/machine-learning-based forecasting methodology in order to accurately predict the performance achievable by transactional applications executing on top of transactional in-memory data stores, in face of changes of the scale of the system. Applications of TAS range from on-line selfoptimization of in-production applications, to the automatic generation of QoS/cost driven elastic scaling policies, and support for what-if analysis on the scalability of transactional applications. We demonstrate the accuracy and feasibility of TAS via an extensive experimental study based on a fully fledged prototypal implementation integrated with one of the most popular open-source transactional data grids (JBoss Infinispan c ©) and industry-standard benchmarks generating a breadth of heterogeneous workloads. Keywords-Transactional data grids; Elastic Scaling; Analytical Performance model; Machine learning.
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