Presentation - Capacity Management for Oracle ExadataDatabase Machine v2

8/12/2019 Presentation - Capacity Management for Oracle ExadataDatabase Machine v2

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Boris Zibitsker, BEZ Systems Predictive Analytics for IT 1

Capacity Management for Oracle

Exadata Database Machine v2

Dr. Boris Zibitsker, BEZ Systems

[email protected]

www.bez.com

OOW 2010


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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.


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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


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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


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Capacity Management Functions

CapacityManagement

StrategicCapacityPlanning

TacticalPerformanceManagement

OperationalWorkload

Management


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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


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Decision Support Techniques

Gut feelings Rules of thumb

Regression analysis

Analytical models

Simulation models

Benchmarks

Workload Growth

ResponseTime SLM

SLOUti l izat ion Law:

U = A * S , where U Utilization,A Arrival Rate,

S Service TimeResponse Time Law:

R = S / (1 U) , where R Response Time. See [1,2]

SLM


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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

1550

25

25

RAC DB Server Grid

Disk

CPUCPU Max?Max? CPU

InfinibandSwitch

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, peakmeasurement 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 usedifferent hardware, software and

DBMS platforms

Workload profile in production

environment will be different

New workload will increasecontention 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 changedproactively

Predict how new

application will affect

performance of

existing applications


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CPU Utilization Increase After

Implementation of New Application

New workload will use more

CPU resources than existing

workloads

Identify proactive

performance managementmeasures


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3. How to justify server consolidation


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How to justify server consolidation

Production DB Machine

WKL2

WKL1

WKL3


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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


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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


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4. What should be tuned?


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Predicted Impact of Data Compression on

Workloads Response Time and Disk Utilization

Data is stored by column andthen compressed

Factors affecting acompression ratio:

Table size

Data cardinality

Read/write ratio

Prediction results show thatdata compression affectsOLTP and DSS workloadsperformance differently


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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 solutionthat will help to meet SLOs for

major workloads


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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 scanfiltrates data

It affects decisions when to

create indexes


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5. What is the optimum level of

workload concurrency?


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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 spendless time waiting for

resources, but spend more

time waiting for the thread

Performance of the workload

with limited concurrencymight suffer, but other

workloads can have

significant performance gain


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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


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6. What is the optimum workload

priority?


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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 evaluatedifferent 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


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7. What is the minimum hardware

upgrade required to support SLOs?


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Predicted Impact of Adding 4 RAC Nodes and 14

Exadata Cells in May 2011 on Workloads

Response Time


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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


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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


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9. How to Coordinate Configuration Planning

for Middle Tier and Oracle Exadata

Database Machine to Support SLOs forMajor Workloads


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Model can be used to find optimum physical and

virtual configurations capable of supporting SLOs

for growing workloads


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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


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Workloads

SLOs, SLAs

Action

Predictive Analytics as a Base for Defining

Optimum Rules for Resource Management

Modeling

Optimization

System

CRM

HR

ETL

Sales

MKT

SLMDecisions


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Thank you!

Contact Info rmation

[email protected]

bezsys.blogspot.com
mailto:[email protected]://www.bez.com/http://www.bez.com/mailto:[email protected]


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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 Optimization6. 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 DatabaseMachine: Exadata v2

10.Oracle Enterprise Manager Grid Control documentation library at:

http://www.oracle.com/technology/documentation/oem.html
http://www.oracle.com/technology/documentation/oem.htmlhttp://www.oracle.com/technology/documentation/oem.html

Presentation - Capacity Management for Oracle ExadataDatabase Machine v2

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