Self
-
tuning
Database
Leili Farzinvash
Nazanin Dehghani
Electrical and Computer Engineering Department
2
Introduction
Self
-
tuning Database
2
3
Outline
Introduction
Characteristics of Autonomic DBMS
Self
-
Tuning Architecture
Alternative Tuning Models
Monitoring Infrastructure
Case studies
Self
-
tuning Database
3
4
Characteristics of Autonomic DBMS
Self
-
optimizing
Self
-
configuring
Self
-
healing
Self
-
protecting
Self
-
organizing
Self
-
inspecting
Self
-
tuning Database
4
5
Phases of Self
-
tuning Loop
Self
-
tuning Database
5
Diagnose
Resole
Observe
6
Self
-
Tuning Architecture
Self
-
tuning Database
6
7
Alternative Tuning Models
Alerter (When to Tune)
Workload as a
Sequence
Dynamic (Online)
Tuning
Self
-
tuning Database
7
8
Monitoring Infrastructure
Query Progress Estimation
Ad
-
hoc Monitoring and Diagnostics
Self
-
tuning Database
8
9
Case Studies
Self
-
tuning Database
9
10
SQL Server
Creating “what
-
if ” physical structures
Search Architecture
Alerter
Self tuning histogram
SQLCM
Self
-
tuning Database
10
11
Database Engine Tuning Advisor (DTA)
Self
-
tuning Database
11
12
Self tuning histogram
Self
-
tuning Database
12
13
DB2
Key Ideas and Themes
Low
-
impact collection of accurate system data
Feedback
Re
-
using Optimizer as a “What if?” tool
Heuristics, new models
Automating batch operation
Self
-
tuning Database
13
14
Future Works
Comparison the quality of automated physical design
solutions
Migration of “heavyweight” to “lightweight”
Distributed tuning and monitoring
Machine learning techniques, control theory, and online
algorithms
Self
-
tuning Database
15
References
1.
Elnaffar, S.; Powley, W.; Benoit, D.; Martin, P. Today's DBMSs: how autonomic are they. Proceedings of
the
14
th International Workshop on Database and Expert Systems Applications (DEXA’
03
),
1
-
5
Sept.
2003
IEEE, Pages:
651
–
655
2.
Surajit Chaudhuri, Vivek Narasayya : Self
-
Tuning Database Systems: A Decade of Progress. VLDB
’
07
, September
23
-
28
,
2007
, Vienna, Austria.
3.
Benoit Dageville ,Karl Dias. Oracle’s Self
-
Tuning Architecture and Solutions : Bulletin of the IEEE
Computer Society Technical Committee on Data Engineering,
2006
.
4.
B. Dageville, M. Zait: SQL Memory Management in Oracle
9
i. VLDB
2002
,
962
-
973
.
5.
Agrawal, S., Chaudhuri, S. and Narasayya, V. Automated Selection of Materialized Views and Indexes
for SQL Databases.
In Proceedings of the VLDB,
Cairo, Egypt,
2000
.
.
6.
Zilio et al. Recommending Materialized Views and Indexes with IBM DB
2
Design Advisor. In
Proceedings of ICAC
2004
.
7.
S.Parekh, K.R.Rose, J.L. Heller stein, S.Lightstone, M.Huras, and V.Chang. Throttling utilities in the
IBM DBS universal database server. In Amer. Control Conf. (ACC),
2004
8.
Eugene Krayzman, Development of self
-
tuning and autonomic databases and latest achievements:
21
st Computer Science Seminar SE
2
-
T
4
-
1
9.
Sanjay Agrawal, Nicolas Bruno, Surajit Chaudhuri, Vivek Narasayya. AutoAdmin: Self
-
Tuning
Database Systems Technology: Bulletin of the IEEE Computer Society Technical Committee on
Data Engineering,
2006
.
10.
Berni Schiefer, Gary Valentin. DB
2
Universal Database Performance Tuning : Bulletin of the IEEE
Computer Society Technical Committee on Data Engineering,
1999
.
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