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Identifier 000409984
Title A measurement based approach to performance prediction in NoSQL systems
Alternative Title Πρόβλεψη της απόδοσης NoSQL συστημάτων με βάση συστηματικές μετρήσεις
Author Καρνιαβούρα, Φλώρα Γ.
Thesis advisor Πλεξουσάκης, Δημήτρης
Reviewer Μαγκούτης, Κώστας
Τσακαλίδης, Παναγιώτης
Abstract The ability to accurately predict the amount and type of resources needed to sustain a desired level of service is an important enabler of goal-oriented application performance management. While the use of systematic measurements for building performance prediction models is a well studied topic, little attention has been paid so far on the application space of data-intensive systems using NoSQL databases. In this thesis we introduce and evaluate a measurement-based approach to performance prediction of data-intensive applications over NoSQL systems. Measurement-based performance prediction approaches are often limited by a relatively narrow range of hardware characteristics available within each organization's private infrastructure. An opportunity to change this fact is the emergence of federated, large-scale, service-oriented research infrastructures, featuring a variety of heterogeneous hardware. This thesis demonstrates accurate measurement-based prediction of Yahoo Cloud Serving Benchmark (YCSB) performance over NoSQL systems in such infrastructures. We consider three regression techniques: Multivariate adaptive regression splines (MARS), support vector regression (SVR), and artificial neural network (ANN) regression. We find that all three techniques achieve performance prediction with average accuracy of over 90%, with MARS yielding the best results. We extend our results focusing on MARS and a virtualized private cloud environment with two NoSQL data stores, MongoDB and RethinkDB. Despite their differences, we find that MARS achieves accurate performance prediction on both data stores with an average accuracy of 95% across cases considered. This result points to the potential of applying our methodology to a broader set of NoSQL systems and deployment environments
Language English
Subject NoSQL data stores
NoSQL βάσεις δεδομένων
Issue date 2017-07-21
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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