Transactional Auto Scaler: Elastic Scaling of In-Memory Transactional Data Grids
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| Titel: | Transactional Auto Scaler: Elastic Scaling of In-Memory Transactional Data Grids |
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| Autoren: | Diego Didona, Paolo Romano, Sebastiano Peluso, Francesco Quaglia |
| Weitere Verfasser: | The Pennsylvania State University CiteSeerX Archives |
| Quelle: | http://www.gsd.inesc-id.pt/~romanop/files/papers/icac12.pdf. |
| Bestand: | CiteSeerX |
| Schlagwörter: | Categories and Subject Descriptors C.4 [Computer Systems Organization, Performance of Systems— Modeling techniques, Measurement techniques, Performance attributes Keywords Analytical Models, Performance Evaluation, Autonomic Provisioning, Distributed Software Transactional Memory |
| Beschreibung: | 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. |
| Publikationsart: | text |
| Dateibeschreibung: | application/pdf |
| Sprache: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.9949 |
| Verfügbarkeit: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.306.9949 http://www.gsd.inesc-id.pt/~romanop/files/papers/icac12.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| Dokumentencode: | edsbas.98FB05BD |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | 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. |
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