Presentation capacity management for oracle exadata database machine v2

© Boris Zibitsker, BEZ Systems Predictive Analytics for IT 1
Capacity Management for Oracle
Exadata Database Machine v2
Dr. Boris Zibitsker, BEZ Systems
boris@bez.com
www.bez.com
OOW 2010

About the Author
Dr. Boris Zibitsker, Chairman, CTO, BEZ Systems.
• Boris and his colleagues developed modeling technology supporting
multi-tier distributed systems based on Oracle, Teradata, DB2, and
SQL Server. Boris consults, and speaks frequently on this topic at
many conferences across the globe.

Focus
• Oracle Exadata Database Machine supports mix OLTP and data
warehouse workloads, which provides a lot of benefits but require
effective capacity management
• Capacity management includes workload management, performance
management and capacity planning
• Knowing you workloads profiles and setting realistic SLOs for each
workload is critical for Exadata DBM capacity management
• Everything is interdependent and workload growth and any change
can improve performance of one of the workloads, but negatively
affect response time of others
• We will review a case study illustrating capacity management solutions
for Oracle Exadata Database Machine supporting mix workload
growth, new applications implementation and server consolidation

©Boris Zibitsker, BEZ Systems Predictive Analytics for IT 4
Oracle Exadata Database Machine v2
Supports Mixed Workloads
• Each workload use one or
several VMs, JVMs and
Application servers
• Each workload use one or
several RAC nodes
• Data spread across all
Exadata Cell Disks

Capacity Management Functions
Capacity
Management
Strategic
Capacity
Planning
Tactical
Performance
Management
Operational
Workload
Management

Capacity Management Process for Exadata DBM
Includes Standard Steps
• Data collection
• Workload characterization
• Setting goals (SLO & SLA)
• Workload forecasting
• Performance prediction
• Workload management
• Performance management
• Capacity planning
• Verification
Capacity
Management

RAC CPU Service
RAC Delay
RAC CPU Wait
Oracle Exadata DBM Response Time
Components
Exadata CPU Wait
InfiniBand
Exadata CPU Service
Exadata Disk Wait
Exadata Flash Cash RT
Exadata Disk Service

Major Factors Affecting Oracle Exadata DBM
Response Time
• Workload profile
• Usage of resources
• Expected growth
• Hardware and software
configuration
• Parallel processing
• Smart scan
• Columnar compression
• Flash cache
Workload Growth
ResponseTime
Constant Service Time (S)
Variable Queueing Time (Q)

SLO, SLA and SLM
• Set realistic SLO - Goal
• Negotiate SLA – Contract
• Organize proactive SLM
Workload Growth
ResponseTime
SLO
SLM

Decision Support Techniques
• Gut feelings
• Rules of thumb
• Regression analysis
• Analytical models
• Simulation models
• Benchmarks
Workload Growth
ResponseTime
SLM
SLO
Utilization Law:
U = A * S , where U – Utilization,
A – Arrival Rate,
S – Service Time
Response Time Law:
R = S / (1 – U) , where R – Response Time. See [1,2]
SLM

Closed Queueing Network Model of Exadata DBM
with Mix Workloads
(many details are not shown)
1
2
n
CPU
Memory
1
2
n
CPU
Disk
Flash
Active Active
Exadata Storage Server Grid
Users
Requests
75
60
15
50
25
25
RAC DB Server Grid
Disk
CPUCPU Max?Max? CPU
Infiniband
Switch
Network
Workloads
• This is a simplified high level analytical Queueing Network Model of the Exadata DBM
• Many details are omitted for illustration purpose
• It is a multi-tier model, where 1st tier represents RAC nodes and 2nd tier represents Exadata cells
• The number of tiers in the model can be expanded to represent middle tier application servers
• Exadata Cells, physical and flash disks, channels, infiniband, etc are represented in the model by
the network of servers and queues
• Measurement data characterizing utilization of RAC nodes and Exadata cells can be extracted from
Oracle OEM

Case Study
1. What will be the impact of the workload and volume
of data growth?
2. What will be the impact of new application
implementation?
3. How to justify server consolidation.
4. What should be tuned?
5. What is the optimum level of workload concurrency?
6. What is the optimum workload priority?
7. What is the minimum hardware upgrade required to
support SLOs?
8. What will be the impact of changing number of
processors per RAC node?
9. How to coordinate configuration planning for middle
tier and Exadata Machine to support SLOs for major
workloads.

1. What will be the impact of the workload
and volume of data growth?

Oracle Exadata Database Machine v2
Environment
• Know profile of each workload
• Analyze cyclical pattern of
resource utilization by each
workload
• Find representative, peak
measurement interval as a base
for further analysis

RAC Nodes Performance Analysis
• Analyze seasonal trends
• Find representative and peak
RAC CPU utilization by each of
the workloads
• Build profile for each workload

Exadata Cells Performance Analysis
• Exadata Cell Flash Disk RT is 1 – 1.5 ms
• Exadata Cell Physical Disk RT is 5 – 10 ms
• Exadata CPU Utilization is 5 – 20%
0.000
5.000
10.000
15.000
1 2 3 4 5 6
Average Cell Disk RT (ms)
Average RT (ms)
0.000
0.500
1.000
1.500
2.000
1 2 3 4 5 6 7 8
Average Flash Disk RT (ms)
Average RT (ms)
0.0
10.0
20.0
30.0
Cell CPU Util %
Cell CPU Util %

Predicting Workload and Volume of Data
Growth Impact on Response Time
• Prediction shows how workload
and volume of data growth will
increase contention for systems
resources and how it will affect
RT of each of workloads
• Find when RT will not meet SLO

Waiting Time for CPU Will Become the Largest
Component of the Sales Response Time
• Find when SLO will not be
met
• Find which workload will use
most of CPU resources
• Identify options how to
improve performance

2. What will be the impact of new
application implementation?

New Application
What will be the impact of implementing a new
workload?
• Test environment can use
different hardware, software and
DBMS platforms
• Workload profile in production
environment will be different
• New workload will increase
contention for resources and
affect current workloads
performance
Production DB MachineStress Testing
New Appl

NewAppl Implementation Will Impact
Performance of Existing Workloads
• Simulation of moving workloads
from test to production system
predict how new workload will
affect performance of the existing
workloads
• Model take into consideration
differences between hardware
and software platforms,
differences in volume of data, etc.
• Set realistic expectations and
justify what should be changed
proactively
Predict how new
application will affect
performance of
existing applications

Predicted NewAppl Response Time After
Implementation on Production Oracle
Exadata DBM
• Prediction results show how new
application will perform in
production environment
• Reduce risk of surprises
• Identify future bottlenecks and
justify proactive performance
management actions

CPU Utilization Increase After
Implementation of New Application
• New workload will use more
CPU resources than existing
workloads
• Identify proactive
performance management
measures

3. How to justify server consolidation

How to justify server consolidation
Production DB Machine
WKL2
WKL1
WKL3

How Server Consolidation Will Affect
Existing Workloads
• Prediction results evaluate the
impact of planned server
consolidation on Oracle
Database Machine Exadata v2
• Shows when system will not
meet SLOs
• Identify the minimum upgrade
required to support SLOs

Predict Response Time for Workloads WKL1 and
WKL2 and CPU Utilization After Consolidation
• Prediction results show how
workloads WKL1 and WKL2 will
perform after server
consolidation and how it will
affect CPU utilization of Oracle
Database Machine Exadata
• Savings in power consumption,
software licenses, maintenance,
vs coexistence on one platform

4. What should be tuned?

Predicted Impact of Data Compression on
Workloads Response Time and Disk Utilization
• Data is stored by column and
then compressed
• Factors affecting a
compression ratio:
• Table size
• Data cardinality
• Read/write ratio
• Prediction results show that
data compression affects
OLTP and DSS workloads’
performance differently

Prediction results can be used to evaluate
options and find solution satisfying SLOs of
major workloads
• Each ASM Disk Group has
different performance
characteristics and cost
• Each table has different size,
frequency of accesses by
different workloads and pattern of
using data
• Modeling can be used to
evaluate different alternatives of
placement data and find solution
that will help to meet SLOs for
major workloads

Exadata Parallel Data Access Change Index
Strategy
• Parallel access to data
distributed across all disks
reduce I/O service time
• Usage of Flash cache and
flash disks, smart scan
filtrates data
• It affects decisions when to
create indexes

5. What is the optimum level of
workload concurrency?

Limiting Concurrency Reduces Contention but
Increases # of Requests Waiting for the Thread
• Limiting Concurrency for the
workload can reduce
contention for resources
• Requests of the workload with
limited concurrency will spend
less time waiting for
resources, but spend more
time waiting for the thread
• Performance of the workload
with limited concurrency
might suffer, but other
workloads can have
significant performance gain

Predicting impact of lowering the level of
concurrency for ETL workload
• ETL use a lot of resources,
but satisfy SLO
• What if we limit ETL
concurrency starting Period
#3?
• ETL time to load data will
increase, but will be
satisfactory
• Response time for other
workloads will improve
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 2 3 4 5 6 7 8 9 10 11 12
Relative Response Time
Sales
Marketing
HR
Archive_1
ETL

6. What is the optimum workload
priority?

What will be an impact of workload priority
change?
• Increase Priority for the
critical Workloads will
Improve their performance
but negatively affect others
• Prediction results evaluate
different alternatives and
provide valuable information
to justify proactive decisions
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1 2 3 4 5 6 7 8 9 10 11 12
Relative Response Time
Sales
Marketing
HR
Archive_1
ETL

7. What is the minimum hardware
upgrade required to support SLOs?

Predicted Impact of Adding 4 RAC Nodes and 14
Exadata Cells in May 2011 on Workloads’
Response Time

Impact of Adding 4 RAC Nodes and 14
Exadata Cells in May 2011 on CPU and Disk
Utilization
• Hardware upgrade reduce
contention for RAC CPU
resources and Exadata Disk
utilization

8. What Will Be the Impact of Changing
Number of Processors per RAC Node
(assuming that Oracle will make a new node announcement)

Predicted Impact of Changing Number of
Processors per RAC Node by 50% in April 2011
(assuming that Oracle will introduce new RAC node with more CPUs per node)
• Increase RAC node capacity
will have positive impact on
response time and reduce
CPU utilization

9. How to Coordinate Configuration Planning
for Middle Tier and Oracle Exadata
Database Machine to Support SLOs for
Major Workloads

Model can be used to find optimum physical and
virtual configurations capable of supporting SLOs
for growing workloads

Predicted Impact of the New VM
• Adding new VM increases contention for resources
• Performance prediction results illustrate the impact of adding
VMs to the same host server and help to generate proactive
capacity planning recommendations

Workloads
SLOs, SLAs
Action
Predictive Analytics as a Base for Defining
Optimum Rules for Resource Management
Modeling
Optimization
System
CRM
HR
ETL
Sales
MKT
SLM
Decisions

Summary
1. Think how to manage a system, not just DBMS
2. Know your workloads profiles (performance, resource utilization and data
usage)
3. Set up realistic SLOs and SLAs for each workload
4. Before you will make capacity planning, performance management or
workload management changes ask yourself “what should I expect?”
5. Workloads and servers are interdependent and planned change can
improve performance of one of the workloads, but negatively affect others
6. Use stress testing and predictive analytics to evaluate alternatives, justify
decisions and set up expectations
7. How can you manage if you do not know what to expect?
8. Compare the actual results with expected and if they are significantly
different find out why
9. Organize a continuous proactive performance management

Thank you!
Contact Information
boris@bez.com
www.bez.com
bezsys.blogspot.com

References
1. E. Lazowska and others “Quantative Systems Performance”
2. L. Kleinrock, “Queueing Systems”
3. B. Zibitsker, A. Lupersolsky , IOUG 2009, “Modeling and Optimization in Virtualized Multi-tier Distributed
Environment”
4. B. Zibitsker, IOUG 2008. “Reducing Risk of Surprises in Changing Multi-tier Distributed Oracle RAC
Environment”
5. B. Zibitsker, DAMA 2007, “Enterprise Data Management and Optimization”
6. B. Zibitsker, CMG 2008, 2009 “Hands on Workshop on Performance Prediction for Virtualized Multi-tier
Distributed Environments”
7. J. Buzen, B. Zibitsker, CMG 2006, “Challenges of Performance Prediction in Multi-tier Parallel
Processing Environments”
8. B, Zibitsker, G. Sigalov, A. Lupersolsky “Modeling and Proactive Performance Management of Multi-tier
Distributed Environments”, International conference “Mathematical methods for analysis and
optimization of information and telecommunication networks"
9. B. Zibitsker, C. Garry, CMG 2009, "Capacity Management Challenges for the Oracle Database
Machine: Exadata v2“
10.Oracle Enterprise Manager Grid Control documentation library at:
http://www.oracle.com/technology/documentation/oem.html

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Presentation capacity management for oracle exadata database machine v2