Cloud Computing - An Evaluation of Rules of Thumb for Tuning RDBMSs

Cloud computing is currently a very attractive environment for IT service provision. The use of virtualization on this architecture has allowed greater flexibility and rationalization of IT infrastructure. Recently, many legacy Relational Database Management Systems (RDBMSs) have been incorporated into cloud environments through virtualization. However, since these systems were not designed for this environment, their configuration methods do not consider and do not adapt to the changes in resource availability. This paper aims to evaluate the use of rules of thumb in RDBMS configuration. Through an evaluation method that simulates concurrent I/O workloads, we analyzed the RDBMS performance under various settings. Our results demonstrate the inefficiency of employing the well-known configuration rules, pointing the need of new definitions for cloud computing environments.

[1]  James E. Smith,et al.  The architecture of virtual machines , 2005, Computer.

[2]  Ashraf Aboulnaga,et al.  Database Virtualization: A New Frontier for Database Tuning and Physical Design , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[3]  Alan Jay Smith,et al.  I/O reference behavior of production database workloads and the TPC benchmarks—an analysis at the logical level , 1999, TODS.

[4]  Shivnath Babu,et al.  Tuning Database Configuration Parameters with iTuned , 2009, Proc. VLDB Endow..

[5]  Prashant J. Shenoy,et al.  Dolly: virtualization-driven database provisioning for the cloud , 2011, VEE '11.

[6]  Christina Delimitrou,et al.  Time and Cost-Efficient Modeling and Generation of Large-Scale TPCC/TPCE/TPCH Workloads , 2011, TPCTC.

[7]  Sam Lightstone,et al.  Adaptive self-tuning memory in DB2 , 2006, VLDB.

[8]  Ashraf Aboulnaga,et al.  Automatic virtual machine configuration for database workloads , 2008, SIGMOD Conference.

[9]  Pengcheng Xiong.  Dynamic management of resources and workloads for RDBMS in cloud: a control-theoretic approach , 2012, PhD '12.

[10]  Anthony K. H. Tung,et al.  A new approach to dynamic self-tuning of database buffers , 2008, TOS.

[11]  Mohamed F. Mokbel,et al.  Exploiting the Impact of Database System Configuration Parameters: A Design of Experiments Approach , 2008, IEEE Data Eng. Bull..

[12]  Le Yi Wang,et al.  VCONF: a reinforcement learning approach to virtual machines auto-configuration , 2009, ICAC '09.

[13]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .