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ACM Transactions on Database Systems (TODS)
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On the analytical modeling of database concurrency control
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The dangers of replication and a solution
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PODS '82 Proceedings of the 1st ACM SIGACT-SIGMOD symposium on Principles of database systems
Analysis of Replication in Distributed Database Systems
IEEE Transactions on Knowledge and Data Engineering
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Resource Allocation for Autonomic Data Centers using Analytic Performance Models
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
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Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
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ACM SIGOPS Operating Systems Review
Autonomic mix-aware provisioning for non-stationary data center workloads
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Proceedings of the 2nd ACM Symposium on Cloud Computing
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Proceedings of the 2nd ACM Symposium on Cloud Computing
Fuzzy Modeling Based Resource Management for Virtualized Database Systems
MASCOTS '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
DISC'06 Proceedings of the 20th international conference on Distributed Computing
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DSOM'05 Proceedings of the 16th IFIP/IEEE Ambient Networks international conference on Distributed Systems: operations and Management
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ACM SIGOPS Operating Systems Review
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Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques
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In this paper we introduce TAS (Transactional Auto Scaler), a system for automating elastic-scaling of in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from on-line self-optimization of in-production applications to automatic generation of QoS/cost driven elastic scaling policies, and support for what-if analysis on the scalability of transactional applications. The key innovation at the core of TAS is a novel performance forecasting methodology that relies on the joint usage of analytical modeling and machine-learning. By exploiting these two, classically competing, methodologies in a synergic fashion, TAS achieves the best of the two worlds, namely high extrapolation power and good accuracy even when faced with complex workloads deployed over public cloud infrastructures. We demonstrate the accuracy and feasibility of TAS via an extensive experimental study based on a fully fledged prototype implementation, integrated with a popular open-source transactional in-memory data store (Red Hat's Infinispan), and industry-standard benchmarks generating a breadth of heterogeneous workloads.