project phase final 2013

8/11/2019 Project Phase Final 2013

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Performance Tuning of

Distributed Databases

Using Machine Learning

Techniques

Guide: Prof. S F Rodd

Presented by:

Project Group B101


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Contents

Introduction Need for tuning

Neural Network Approach to tuning

Memory Architecture of DB2 andOracle10g

Benchmark Factory Tool Overview

Distributed Database Environment

Distributed Tuning Architecture

GUI Snapshots

References2


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Introduction

Every enterprise uses DBMS to manage its dayto day functioning, forecast future growth and

develop strategies based on past data.

Wide spread use of Web Applications that are

database driven

Success of these apps. Depends on speed withwhich the services are provided

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Need for Tuning Failure to provide the published web service causes huge

business losses.

Flash crash of NYSE in may 2010 wiped out $862billion injust 20 minutes.

Manual tuning is expensive

According to payscale.com, a DBA earns on an avg anywherebetween Rs 1,69,242 - Rs 10,17,210

Changing/upgrading IT infrastructure every 2-3 years isexpensive and time consuming

Hence, there is a need to maximize the application of systemresources in an attempt to execute transactions as efficiently andquickly as possible

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Architecture

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DB2 Memory Architecture

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A Closer look at the Shared

Memory

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parameters

Command to change the Buffer Pool Size

SQL> db2 -v alter bufferpool IBMDEFAULTBP size 4000

Command to check whether bufferpool has been altered

SQL>select * from syscat.bufferpools

BPNAME BUFFERPOOLID KSIZE NGNAME NPAGES PAGESIZE ESTORE

---------- ----------- ------------ ------ --------- -------- -----------

IBMDEFAULTBP 1 0 - 4000 4096 N

To change the LOCKHEAP parameters, use the following commands:

SQL> db2 -v update db cfg for DB_NAME using LOCKLIST a_number

SQL> db2 -v update db cfg for DB_NAME using MAXLOCKS b_number

SQL> db2 -v update db cfg for DB_NAME using LOCKTIMEOUT c_number

To change the values of SORTHEAP

SQL> db2 -v update db cfg for DB_NAME using SORTHEAP a_value9


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Oracle10g Memory Architecture

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Auto-tuning features in Existing

DBMS Auto-tuning feature is available in Oracle 10g SQL> show parameter sga_target;

NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

sga_target big integer 0

SQL> show parameter sga_max_size; NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

sga_max_size big integer 164M

SQL> alter system set sga_max_size=1200M scope=spfile;

SQL> alter system set sga_target=1100M scope=both; NAME TYPE VALUE

------------------------------------ ----------- ------------------------------

sga_target big integer 1104M

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Benchmark Factory Tool

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s r u e a a aseEnvironment

Types Homogeneous

Heterogeneous

Applications accessing data across databases

using Database links Link creation:

CREATE DATABASE LINK foo CONNECT TO scott

IDENTIFIED BY tiger USING 'rev';

SELECT * FROM emp@foo

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Distributed Tuning Architecture

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GUI Snapshots

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10g

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Response Time Graph For DB2

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 10 20 30 40 50 60 70

Avg. Response Time (sec)

Avg. Response Time (sec)

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CONCLUSION

The proposed self tuning architecture enhances theperformance of the DBMS

On distributed databases in Oracle 10G our approach

yields a significant improvement in performance as

compared to in-built Auto-tuning feature of Oracle.

NN-based tuning approach on centralized DB2

database yields a significant improvement in

performance as compared to self-tuning feature of

DB2.

NN-based tuning approach is a generic one and can

be used to tune any DBMS.

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Future Plan

Implement NN tuning on DB2 on standalone

server and extend the same to Distributed

environment

Test on other DBMS like MSSQL Server,MySQL to validate the method and make it

more generic approach.

Develop a self-tuning approach based on other

machine learning techniques like statisticalapproach.

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References[1]Fuzzy Controlled Architecture for PerformanceTuning of Database Management System by SF Rodd ,

Umakant P Kulkarni, A.R. Yardi

[2]Enhanced Performance of Database by Automated Self-Tuned Systems by Ankit Verma.

[3]Adaptive Self-Tuning Memory in DB2 by Adam J. Storm, Christian Garcia-Arellano,

Sam S. Lightstone, Yixin Diao , M. Surendra .

[4]A New Approach to Dynamic Self-Tuning of Database Buffers by DINH NGUYEN TRAN, PHUNG

CHINH HUYNH, Y. C. TAY and ANTHONY K. H. TUNG.

[5]AutoAdmin: Self-Tuning Database Systems Technology by Sanjay Agrawal, Nicolas Bruno, Surajit

Chaudhuri, Vivek Narasayya

[6] Sanjay Agarwal, Nicolas Bruno, Surajit Chaudhari,AutoAdmin: Self Tuning Database System

Technology, IEEE Data Engineering Bulletin, 2006.

[7] Wiese, David; Rabinovitch, Gennadi,Knowledge Management in Autonomic Database Performance

Tuning, 20-25 April 2009.

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THANK YOU

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project phase final 2013

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