feifei chen swinburne university of technology melbourne, australia automating performance and...
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Feifei Chen
Swinburne University of Technology
Melbourne, Australia
Automating Performance and Energy Consumption Analysis for Cloud Applications
CRICOS Provider: 00111D | TOID: 3059
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
• Background• Problem Analysis• Approach• Evaluation
2
Outlines
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN 3
Cloud Computing
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN 4
Benefits of Cloud
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN 5
Dark Side of Cloud
High CO2 emissions contribution
Gartner Report 2007: IT industry contributes 2% of world's total CO2 emissions
New York Times 2012: Data centres use about 30 billion watts of electricity per hour worldwide, equivalent to the output of about 30 nuclear power plants
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN 6
Dark Side of CloudHigh Operational Cost
U.S. EPA Report 2007: 1.5% of total U.S. power consumption used by data centers which has more than doubled since 2000 and costs $4.5 billion
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
Green Cloud Computing
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Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
Service Level Agreement (SLA)
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Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN 9
A key objective of cloud service providers:
Develop solutions to cloud application deployment and management with minimum energy consumption while still guaranteeing performance and other Service Level Agreement (SLA) targets.
Energy Consumption
PerformanceService Providers
Understand both system performance and energy
consumption pattern
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
• Background• Problem Analysis• Approach• Evaluation
10
Outlines
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
Understand both system performance and energy consumption pattern: • running extensive experiments with heterogeneous
parameters/metrics and workloads;• collecting appropriate cloud and application
energy/performance measurements;• performing energy/performance trade-off analysis.
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Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN 12
JPetStore Deployment
1000 User
s
Browse Product5000User
s
Workload
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
• Manually performing the tasks is tedious and time-consuming
• Cloud system performance is related to• Architecture• Workload
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Challenges
An automated performance and energy consumption evaluation framework is imperative
The framework should be able to accommodate different cloud system architectures and adopt
different application workloads during load tests and the trade-off evaluation process
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
• Background• Problem Analysis• Approach• Evaluation
14
Outlines
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Automated Performance and energy analysis tool - StressCloud
Deploy Load Test Services
Deploy Load Test Services
Load Test Scripts
Model Cloud System Workload
Model Cloud System Workload
Stored ResultsStored Results
Performance Engineer
Model Cloud Architecture
Model Cloud Architecture
Workload Deployment Scripts
Generate Load Test Scripts
Generate Load Test Scripts
Generate Workload Deployment ScriptsGenerate Workload Deployment Scripts
Workload Model
Cloud System Architecture
Model
Apply Load Tests to Cloud
Apply Load Tests to Cloud
Test Results
Collect Performance and
Energy Data
Collect Performance and
Energy Data
Visualise ResultsVisualise Results
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StressCLoud
Cloud
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Automated Performance and energy analysis tool - StressCloud
Cloud Architecture Model:
All available resources in the target cloud system and their detailed configurations.
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Automated Performance and energy analysis tool - StressCloud
Cloud Application Workload Model: A set of Tasks modelling the target cloud application behaviour
Computation-Intensive CPU-Intensive Memory-Intensive
Data-Intensive Communication-Intensive
Task: A stochastic form chart specifying the detailed user requests and required responses from the cloud system
Task Type Service Type in StressCloudCPU-intensive Fibonacci sequence calculatingMemory-intensive File processingData-intensive Rational database operatingCommunication-intensive HTTP request/response
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High-level Workload Model of JPetStore
Text lineStochastic Form-Chart ExampleOf Each Task
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Architecture ModelCloud System Architecture
Model Example
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Workload Deployment
Scripts Example
Text lineLoad Testing
Scripts Example
Text lineVisualized Results
Swinburne
SCIENCE | TECHNOLOGY | INNOVATION | BUSINESS | DESIGN
• Background• Problem Analysis• Approach• Evaluation
24
Outlines
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Experiment SetupEnergy and performance profiling framework
VM configuration
Virtual Machine
Number of Cores RAM Hard Disk
Small 1 2GB 80GB Medium 2 4GB 80GB Large 3 6GB 80GB
XLarge 4 8GB 80GB
Cloud Server
VirtualMachine VirtualMachine
Apache Tomcat
…...Load Test Web Service
MS SQL Server
Apache Tomcat
Load Test Web Service
MS SQL Server
Monitor and Load Generator
StressCloud
PowerNodeGreenWave Gateway
Router
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Experiment ResultsTest set 1: Keep the resource allocation strategy constant
while changing workload
System Configurations: 1 Large VM (3CPUs and 6GB RAM)
Workload: user workload 50~200
Energy Consumption and Throughput
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Experiment ResultsTest set 2: Keep the workload constant while changing the
resource allocation strategy
System Configuration: 1Large: Deploy three types of tasks on the 1 VM.3Small(D): Deploy three types of tasks on different VMs.3Small(S): Deploy three types of tasks on the same VM with workloads evenly distributed across all VMs.
Workload: user request 100.
Energy Consumption and Throughput
