Performance Aware DevOps. Giuliano Casale Department of Computing (DoC) Imperial College London
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1 Performance Aware DevOps Giuliano Casale Department of Computing (DoC) Imperial College London
2 The Road to Agility Agile development is a success story But development (Dev) hits a bottleneck during operations (Ops) Programmable infrastructures provide a solution (e.g., Puppet, Chef, ) 1/24/2016 G. Casale Performance Aware DevOps 2
3 Is DevOps Just IT Automation? Automation is the answer The question is how to improve: Collaboration of Dev & Ops Sharing of responsibilities System oriented mindset 1/24/2016 G. Casale Performance Aware DevOps 3
4 Embracing DevOps Before DevOps Development IT Operations Development Tools Testing Unified Processes Unified Tooling Tools Production DevOps QA Quality Assurance Development & IT Operations Tools Development Testing Production Continuous Integration Continuous Testing Continuous Monitoring Source: What is DevOps? E. Di Nitto et al. 1/24/2016 G. Casale Performance Aware DevOps 4
5 Quality Assurance in Today s DevOps Mainly functional testing What about performance tests? Just 13% companies run automated performance tests within continuous integration servers [Neotys] 1/24/2016 G. Casale Performance Aware DevOps 5
6 Performance Engineering (PE) SPE Requirements SLAs Cost Tradeoffs Compare Alternatives? Profiling Load testing User behaviour Regression Incident Analysis Bottleneck Identification Root Cause Analysis Monitoring Deployment Capacity Management Adaptation 1/24/2016 6
7 A New Challenge? Somewhat similar to early 2000s Dev teams write UML models PE focuses on analytical models How to facilitate the interaction? Performance Model Platform Independ. Model Architecture Model Platform Specific Model Model Driven Engineering (MDE) is the only game in town 1/24/2016 G. Casale Performance Aware DevOps 7 Java
8 Performance Aware MDE Performance Annotations UML MARTE profile, UML DAM profile Usage Profile System Behaviour Failure Probability 1/24/2016 G. Casale Performance Aware DevOps 8
9 Model to Model Transformation From Architecture to Analytical Models 1/24/2016 G. Casale Performance Aware DevOps 9
10 Model to Model Transformation From Architecture to Analytical Models adminserver CPU database CPU User Think Time User behaviour 1 (class 1) 1s.3s HTTPagent.7s User behaviour 2 (class 2) SVNagent 2s Throughputs Response times Resource Utilization 1/24/2016 G. Casale Performance Aware DevOps 10
11 So What About Continuous Delivery? Measurement Driven DevOps incident report Ops Tomcat 1 MySQL 1 release chef MySQL 2 Dev jenkins (performance unit tests) Users Tomcat 2 MySQL 3 incident report 1/24/2016 G. Casale Performance Aware DevOps 11
12 So What About Continuous Delivery? Today s Performance Aware MDE methods early-stage performance assessment Ops incident report Tomcat 1 MySQL 1 chef release MySQL 2 Dev jenkins (performance unit tests) Users Tomcat 2 MySQL 3 incident report 1/24/2016 G. Casale Performance Aware DevOps 12
13 So What About Continuous Delivery? Performance Aware DevOps continuous performance engineering (a shared system view ) Ops incident report Tomcat 1 MySQL 1 chef release MySQL 2 Dev jenkins (performance unit tests) incident report & model correlation Users Tomcat 2 MySQL 3 continuous parameterization 1/24/2016 G. Casale Performance Aware DevOps 13
14 Performance Aware DevOps SPE Requirements SLAs Cost Tradeoffs Compare Alternatives continuous performance engineering Profiling Load testing User behaviour Regression Incident Analysis Bottleneck Identification Root Cause Analysis Monitoring Deployment Capacity Management Adaptation 1/24/
15 Research Roadmap 1. Shared MDE models for both Dev and Ops 2. Extensions of existing tools for performance validation to model Ops environment 3. Continuous parameterization methods 4. Traceability and anomaly detection 1/24/2016 G. Casale Performance Aware DevOps 15
16 Research Roadmap 1. Shared MDE models for both Dev and Ops Annotations for recent technologies: Cloud computing Internet of Things Cyber Physical Systems Mobile devices Big data Some relevant outputs from large EU projects 1/24/2016 G. Casale Performance Aware DevOps 16
17 Research Roadmap 1. Shared MDE models for both Dev and Ops 2. Extensions of existing tools for performance validation to model Ops environment 3. Continuous parameterization methods 4. Traceability and anomaly detection 1/24/2016 G. Casale Performance Aware DevOps 17
18 Extension of Existing Tools Model Driven Prediction Tools: LQNS, LINE, PerfCenter, JMT,... CPU Steal VM VM my VM VM Cloud CPU steal model Lo Hi Virtualization Layer Physical Host Spot instance price Up Kill Simulation & Analytical Models Challenge: model new resource types (e.g., spot & micro VM, ) and platform services 1/24/2016 G. Casale Performance Aware DevOps 18
19 Extension of Existing Tools Performance Antipatterns Tools: PANDA Aemilia Challenge: create libraries of anti patterns for deployment models relevant to Ops References: Trubiani et al. Pattern-based Identification and Refactoring 1/24/2016 G. Casale Performance Aware DevOps 19
20 Research Roadmap 1. Shared MDE models for both Dev and Ops 2. Extensions of existing tools for performance validation to model Ops environment 3. Continuous parameterization methods 4. Traceability and anomaly detection 1/24/2016 G. Casale Performance Aware DevOps 20
![Continuous Parameterization Resource Consumption Inference Tools: LibReDe, Filling the Gap (FG) Utilization Utilizzo CPU [0-100%] - % 0 20 40 60 80 100 Ripartizione](/docs-images/87/95845907/images/21-0.jpg "dei Costi Lun Mo Mar Tu Mer We Gio Th Ven Fr Sab Dom Su Usage & Costs Splitting Challenges: inference synchronized to new commits; inference for all model parameters")
21 Continuous Parameterization Resource Consumption Inference Tools: LibReDe, Filling the Gap (FG) Utilization Utilizzo CPU [0-100%] - % Ripartizione dei Costi Lun Mo Mar Tu Mer We Gio Th Ven Fr Sab Dom Su Usage & Costs Splitting Challenges: inference synchronized to new commits; inference for all model parameters 21
22 Research Roadmap 1. Shared MDE models for both Dev and Ops 2. Extensions of existing tools for performance validation to model Ops environment 3. Continuous parameterization methods 4. Traceability and anomaly detection 1/24/2016 G. Casale Performance Aware DevOps 22
23 Performance Traceability Model Driven Monitoring Tools: Kieker AOP used to correlate monitoring and models Challenges: low overhead continuous monitoring, model based monitoring config. References: Van Hoorn et al.; 23
24 Model Traceability Tracking Runtime Adaptations Tools: High level commands such as scale out, deploy, load a model A set of connectors to communicate with IaaS and PaaS providers and Cloud services Web Socket REST Facade MOF CMD Notifier Model Deployer Cloud API Connectors Shell Application Deployment Model Based on the model dependencies compute the list of actions to be performed by the connectors Challenges: model co evolution and adaptivity Reference: Ferry et al. 24
25 Performance Anomaly Detection Application Performance Signatures Methods: ACE, Demand Based,... Utilization Samples Challenge: continuous model aided generation of performance signatures References: Sansottera et al.; Cherkasova et al. Throughput 25
26 A Case Study MODAclouds Project 9M ( ) DevOps inspired tools for multi cloud software Tools: 26
27 MDE for Multi Cloud Software Functional Modelling CCIM CIM Access rating agencies Order analysis Get stock prices Place order Wait for ack from the stock market OK Fail Update customer trading account
28 MDE for Multi Cloud Software QoS Requirements CIM CCIM Access rating agencies Order analysis Get stock prices Response time <0.5s Place order Availability 24h/day Wait for ack from the stock market Fail OK Update customer trading account
29 MDE for Multi Cloud Software Platform Independent Models CPIM Access rating agencies Order analysis Get stock prices A Reliable Resource C Reliable Resource B High perf. Resource key-valued DB Place order Wait for ack from the stock market OK Fail Update customer trading account 1/24/2016 G. Casale Performance Aware DevOps 29
30 MDE for Multi Cloud Software Platform and Technology Specific Models CPIM Access rating agencies Order analysis Get stock prices A Reliable Resource C Reliable Resource B High perf. Resource key-valued DB Place order CPSM SimpleDB Wait for ack from the stock market Fail OK Update customer trading account A-1 Medium CPU Instance Inst. numb. >2 C-1 Large Memory Instance B-1 Large CPU Instance A-2 Worker Role Large C-2 Worker Role Extra Large Table Storage B-2 Worker Role Large 1/24/2016 G. Casale Performance Aware DevOps 30
31 DevOps Inspired Approach Feasibility Study Engineer Application Developer QoS Engineer App Provider Resource model DB (incl info of providers) Venues 4Clouds (Decision Support System) MODACloudML Modelling tool Data Mapping tool Creator 4Clouds (design time IDE) Space 4Clouds (QoS modelling and analysis tool) CloudML 4Clouds (Deployment and Provisioning tool) Dev Filling the Gap Analysis Ops Cloud App Admin Runtime GUI Energizer4Clouds (runtime platform) Tower 4Clouds (Monitoring) Runtime Engine SpaceOps 4Clouds (Self Adaptivity) Addapters 4Clouds (Runtime Services) Cloud App Cloud App 31
32 Ops Aware MDE Functional Modelling MDE with cloud annotations: (MODA)CloudML MDE extensions to describe cloud computing 32
33 Ops Aware MDE Example: Amazon RDS 1/24/
34 Traceability Configurable Monitoring Rules CPIM Deployment Diagram Monitoring Rule Monitoring Annotation Violation Trigger 1/24/2016 Giuliano Casale Department of Computing 34
35 Search Based Deployment Service and QoS model SPACE4Clouds+LINE Amazon Azure Suggest deployment Types of VMs Number of VMs Creator 4Clouds IDE SPACE4Clouds Optimization Engine Quantitative Analysis Optimization QoS & SLA Venues 4Clouds Candidate Cloud Providers Performance Engine 1/24/2016 Amazon Google Azure Transformation to Queueing Networks LINE fluid solver SLA percentiles
36 Search Based Deployment SPACE4Clouds SPACE4Clouds MODACloudML Model Initial Solution Builder Initial solution Optimized Solution VIDEO MILP Solver connector Cost and Feasibility Evaluator Solution Manager Multi thread connectors VM startup line5_2_b.mp4 times MILP solver LINE CPU steal SLAs Reference: D. Ardagna et al. A Multi-Model Optimization Framework for the Model Driven Design of Cloud Applications. SSBSE /24/
37 Conclusion 1/24/2016 G. Casale Performance Aware DevOps 37
38 Questions?
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