TVD-CapMan is capacity management software that collects metrics on CPU, I/O, memory usage and other resources from Oracle databases. It analyzes the data to identify resource shortages and spare capacities, perform trend analysis and predictions, and make recommendations for database distribution and consolidation across host servers. The software was demonstrated through examples showing reports on metric trends over time, predictions, sizing recommendations, and visualizations of resource usage across a database environment.

1. BASLE BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENEVA
HAMBURG COPENHAGEN LAUSANNE MUNICH STUTTGART VIENNA ZURICH
CAPACITY MANAGEMENT
WITH TVD-CapManTM
RECENT PROJECTS AND FEATURES
ROBERT KRUZYNSKI

2. AGENDA
1. INTRODUCTION TO CAPACITY MANAGEMENT
2. INTRODUCTION TO TVD-CapManTM
3. EXAMPLES FROM RECENT PROJECTS
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3. INTRODUCTION
TO CAPACITY MANAGEMENT
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4. Capacity Management
A process to ensure that capacity of database systems
– meets current and future business requirements
– in a cost-effective manner
The goals are
– avoid resource shortages
• they may result in performance and stability problems
– avoid wastage of resources and overcapacity
• negative influence on TCO
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5. What resources are we talking about?
These are the most relevant resources when doing Capacity Management
– CPU usage
• database instance
• database user/application
• server
– IO-Rate, IO-Throughput
• differentiated by reads and writes, small and large operations
• including the I/O category (backup, redo logging, archiving, data file, etc.)
– Memory usage
• database instance: SGA, PGA, process memory
• server: busy/free memory, swap space, huge page
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6. Capacity Management Approach (1)
Record the usage of relevant resources
– in bigger environments or on complex database systems we suggest to install TVD-
CapManTM
Look for resource shortages
– high CPU busy
– high memory usage
– high IO rates (e.g. small SGAs with high IO rates, small DBs with high IO rates)
Look for spare capacities
– low CPU busy
– low memory usage
– large instances with potential to decrease the SGA size
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7. Capacity Management Approach (2)
Find the top consumers
– databases or database-applications
– most important: CPU and IO
Perform proactive performance analysis on top consumers
– implement and document performance tuning activities and resulting changes
– control their impact on the usage of resources
If applicable: check utilization of clustered systems
– can one node handle the whole load?
– control the memory (SGA+PGA) and the number of processes
– control CPU and IO usage
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8. Capacity Management Approach (3)
After some time
– look at the trend
– check the impact of accomplished changes
– repeat capacity analyses
Further steps may include
– improving the distribution of the systems
– supporting consolidation activities
– sizing new systems
– forecasting capacity needs
– implementing performance monitoring
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9. INTRODUCTION TO TVD-CapManTM
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10. TVD-CapManTM
Motto
– enterprise wide capacity-, resource- and performance management, consolidation,
sizing and accounting of Oracle database systems
Features
– collects data about servers, database instances and optionally about application
sessions
– processes and stores collected data and executes predefined reports
– allows various analyses including trend and forecast
– allows distribution and consolidation computations
– shows a big picture of your database environment
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11. TVD-CapManTM
Technical features
– uses only standard Oracle features (no extract cost features)
– supports Oracle >= 8.1.7, including multitenant 12c
– gathering of up to 500 databases on 50 servers per minute
– data gathering is agentless
– collector gathers over SSH or using DB-Links
– data is stored in a repository schema
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12. Collected Metrics
Metrics are collected or aggregated on server, database and applications levels and include
– CPU time consumed by databases and applications
– IO consumed by databases and applications
– redo volume, user calls, transactions, executions, DB time, number of sessions, number
of logons per database and per application
– SGA memory of database instances
– PGA memory consumed by databases and applications
– server's load, CPU usage, memory, huge page memory and swap usage
– database space total, used, free
– wait time per wait class per database and per application
– user-defined metrics can be configured, collected and displayed in the GUI
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13. EXAMPLES FROM RECENT
PROJECTS
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14. Feature: Periodic Reports
Customizable reports
– Time-Series charts
– Spread sheets
Configuration
– Periods (weekly, monthly, yearly, all-time)
– Server/database groups
– Optional prediction
Customization
– Adding/removing lines to charts
– Adding/removing columns to sheets
– Defining new reports
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15. Prediction Report Example
Prediction is based on linear regression analysis, yearly trend considered
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16. Prediction Report Example
Moving average added
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17. T-Shirt Report Example
Used in a migration project for sizing of target servers
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18. T-Shirt Report Example
Uses a custom T-Shirt function (PL/SQL) that implements customer standards
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19. Interactive Status Reports
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Shows average and
trend values per
server, database or
database instance
Easy filtering
Allows definition of
server and instance
groups

20. Trend Line Example
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A trend line
displayed on every
chart
Numeric trend value
allows to search for
systems with
growing or falling
values

21. Distribution & Consolidation Algorithms
Algorithms
– Distribution from scratch - fixed system count
– Distribution minimal invasion - current system count
– Consolidation from scratch - minimal system count
Input
– A list of database instances
– Time range
– Constraints (e.g. number of CPUs, RAM Size, maximum IO.rate/ throughput)
– Grouping type (standalone instance, RAC node, RAC cluster)
Output
– A list of database instances with affiliation to systems
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22. Distribution & Consolidation Algorithms
Multiple statistics can (and should) be used as criteria and constraints
– weight can be added
– normalization factor is computed automatically
SQL> SELECT display_name,weight,value_max_limit,value_percentile_limit,normalization_factor
FROM stat s, optim_stat os WHERE s.stat_id=os.stat_id AND optim_id=21;
DISPLAY_NAME WEIGHT VALUE_MAX_LIMIT VALUE_PERCENTILE_LIMIT NORMALIZATION_FACTOR
-------------------- ---------- --------------- ---------------------- --------------------
total physical requests 2 5000 .048396502
DB CPU total 2 8 40.2723377
DB total space 1 .006282116
SGA size 0 16 1.57759328
DB total memory 1 32 1.17342449
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23. Distribution & Consolidation Algorithms
The key of the algorithms is the rating of an allocation
An allocation describes which elements (database instances) are allocated on which
systems
The rating is computed for each situation and compared with previous ratings
– lowest rating is best
• only if all elements could be allocated
– rating formula
• sum of the standard deviation of all normalized, weighted statistics curves of all
allocations
• a statistic curve is described by the sum of its average and standard deviation
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24. Distribution Example
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25. Distribution Example
Relevant time frame: last week in July
CapMan generates a list of instances to be
moved from current to future allocation
DB Name Current Allocation Future Allocation
F115 3 1
P111 0 1
P115 2 1
P117 6 1
P124 3 1
P133 6 2
P133 3 2
P134 0 1
P135 6 1
P135 3 1
P137 0 1
P140 0 2
P141 6 3
P142 2 1
P144 2 1
P153 6 1
P179 0 3
P180 2 3
P191 2 1
P193 0 2
P222 0 2
P235 0 1
P250 6 2
P250 3 2
P251 6 2
P255 6 2
P265 0 2
P271 0 2
P272 0 1
P290 0 2
P95 0 2
P998 0 3
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26. Big Picture Example
Color: AVG(CPU busy)
Area: AVG(DB CPU)
P1342
P1801
P2301
P971
P1802 P1912
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27. Big Picture Example
Color: MAX(CPU busy)
Area: AVG(DB CPU)
P1342
P1801
P2301
P971
P1802 P1912
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28. IO-Statistics per File Type
Datafile-IOPS ~ 53% of
total IO
Controlfile IOPS ~ 19%
of total IO
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29. Questions & Answers
Roland Stirnimann
Business Development Manager
roland.stirnimann@trivadis.com
Phone +41 58 459 52 47
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Robert Kruzynski
Principal Consultant / Partner
robert.kruzynski@trivadis.com
Phone +49 89 99 27 59 30
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