QoSMOS: QoS metrics management tool suite

Abstract Researchers are unanimous that the database management system (DBMS) is one of the most promising candidates to be the backbone of quality-conscious information services. Usually, this quality is measured by subjective metrics that can be interpreted as the degree to which a DBMS possesses a given functionality that affects its quality. Note that the Quality of Service (QoS) concerns all phases of the designing database application life-cycle, with a particular interest to the physical design phase – considered as the funnel of other phases. The satisfaction of the QoS in the context of the physical design is ensured by metrics. A metric can be seen as a function returning a single numerical vale whose input parameters belong to (i) the database application (including its schema and queries), (ii) the DBMS hosting that database and (iii) the deployment platform. These metrics are usually expressed by analytical cost models whose development is time consuming and requires calibration to reflect the evolution of the database technology (that refers to both software and hardware). Face to this situation, the presence of management tools dedicated to the construction, exploitation and calibration of cost models becomes a necessity for researchers and students. In this paper, we first propose a domain specific language, called CostDL, to develop cost models for QoS-enabled database physical design. The development of such a language necessitates the explicitation of the different entries of a cost model thanks to meta-modeling techniques. Secondly, to increase their reuse, a persistent repository is proposed to store the new developed cost models. The two contributions are regrouped in a framework called QoSMOS providing functionalities related to the management of cost models.

[1]  Stuart Kent.  Model Driven Language Engineering , 2003, Electron. Notes Theor. Comput. Sci..

[2]  Remco M. Dijkman,et al.  APROMORE: An advanced process model repository , 2011, Expert Syst. Appl..

[3]  David Taniar,et al.  The use of Hints in SQL-Nested query optimization , 2007, Inf. Sci..

[4]  Martin Bichler,et al.  Reproducible experiments on dynamic resource allocation in cloud data centers , 2016, Inf. Syst..

[5]  Surajit Chaudhuri,et al.  Table of Contents (pdf) , 2007, VLDB.

[6]  Ladjel Bellatreche,et al.  A Recommender System for DBMS Selection Based on a Test Data Repository , 2016, ADBIS.

[7]  Luca Padovani,et al.  Mathematical Knowledge Management in HELM , 2003, Annals of Mathematics and Artificial Intelligence.

[8]  Ladjel Bellatreche,et al.  MetricStore repository: on the leveraging of performance metrics in databases , 2017, SAC.

[9]  Ladjel Bellatreche,et al.  Towards a Model-based Collaborative Framework for Calibrating Database Cost Models , 2017, ER Forum/Demos.

[10]  Thomas Heinis,et al.  PARINDA: an interactive physical designer for PostgreSQL , 2010, EDBT '10.

[11]  Ilia Petrov,et al.  Making cost-based query optimization asymmetry-aware , 2012, DaMoN '12.

[12]  Martin L. Kersten,et al.  Generic Database Cost Models for Hierarchical Memory Systems , 2002, VLDB.

[13]  Enrique Chavarriaga,et al.  An approach to build XML-based domain specific languages solutions for client-side web applications , 2017, Comput. Lang. Syst. Struct..

[14]  Sebastian Bress,et al.  The generalized physical design problem in data warehousing environment: Towards a generic cost model , 2013, 2013 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[15]  Ladjel Bellatreche,et al.  Parallel and Distributed Data Warehouses , 2018, Encyclopedia of Database Systems.

[16]  Amine Roukh,et al.  A Meta-advisor Repository for Database Physical Design , 2016, MEDI.

[17]  Yannis E. Ioannidis,et al.  Query optimization , 1996, CSUR.

[18]  Andrew Stellman,et al.  Applied software project management , 2005 .

[19]  Ladjel Bellatreche,et al.  CostDL: A Cost Models Description Language for Performance Metrics in Database , 2016, 2016 21st International Conference on Engineering of Complex Computer Systems (ICECCS).

[20]  Boris Kozinsky,et al.  AiiDA: Automated Interactive Infrastructure and Database for Computational Science , 2015, ArXiv.

[21]  Juan de Lara,et al.  Ann: A domain-specific language for the effective design and validation of Java annotations , 2016, Comput. Lang. Syst. Struct..

[22]  Salvador Abreu,et al.  Domain-specific languages in Prolog for declarative expert knowledge in rules and ontologies , 2018, Comput. Lang. Syst. Struct..

[23]  Jennifer Widom,et al.  Database System Implementation , 2000 .

[24]  Sam Lightstone,et al.  DB2 Design Advisor: Integrated Automatic Physical Database Design , 2004, VLDB.

[25]  Bingsheng He,et al.  Revisiting Co-Processing for Hash Joins on the Coupled CPU-GPU Architecture , 2013, Proc. VLDB Endow..

[26]  Lin Ma,et al.  Self-Driving Database Management Systems , 2017, CIDR.

[27]  Frédéric Madiot,et al.  Eclipse Sirius Demonstration , 2015, P&D@MoDELS.

[28]  Frank Budinsky,et al.  Eclipse Modeling Framework , 2003 .