SFScon 22 - Andrea Janes - Scalability assessment applied to microservice architectures.pdf
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The document summarizes research on assessing the scalability of microservice architectures. It discusses how microservices introduce challenges for monitoring performance and reliability due to their decentralized nature. The researcher aims to develop approaches to identify bottlenecks, anomalies, and anti-patterns in microservices. The document outlines a framework called PPTAM that generates load tests to analyze the performance of different architectural configurations and identifies the most scalable option based on success rates under various workloads. Ongoing work also looks to recognize common anti-patterns that can degrade microservice performance.
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SFScon 22 - Andrea Janes - Scalability assessment applied to microservice architectures.pdf
- 1. 1 Scalability assessment applied to microservice architectures Andrea Janes FHV Vorarlberg University of Applied Sciences 11. November 2022
- 2. 2 The starting point – “The term ’Microservice Architecture’ has sprung up over the last few years to describe a particular way of designing so ware applications as suites of independently deployable services. While there is no precise definition of this architectural style, there are certain common characteristics around organization around business capability, automated deployment, intelligence in the endpoints, and decentralized control of languages and data1 .” 1 Martin Fowler 2014: Microservices, https://martinfowler.com/articles/ microservices.html
- 3. 3 Common representation of such architecture Front-end Service registry Micro- service3 Micro- service4 Micro- service1 Micro- service2
- 4. 4 Continuous delivery is key – Automation is needed to operate such a “system of systems” (e.g., to use “blue/green deployment”) – Monitoring is crucial: – failures in one service can cause degradation in another – timing issues have a higher impact than in a monolith – debugging becomes complicated – Verifying and guaranteeing quality requirements becomes harder. – Our research goal is to develop approaches to identify performance bottlenecks, detect anomalies, and recognize anti-patterns in a DevOps setting.
- 5. 5 PPTAM: Overview (container diagram)2 PPTAM record performance tests pass/fail outcome Test Engineer performs qualiy assurance, release management Product manager visualize tries to isolate problems Software developer APM Tools Dashboard Driver Testbed retrieves Analysis Architect Repository looks for architectural alternatives reads to generate tests plan stores test results deploys/ queries 2 https://github.com/pptam/pptam-tool
- 6. 6 Application Performance Monitoring Micro- service1 Micro- service2 Front-end Service registry User
- 7. 7 Application Performance Monitoring Micro- service1 Micro- service2 Front-end Service registry User Front-end Service registry Micro-service1 Micro-service2
- 8. 8 Application Performance Monitoring Micro- service1 Micro- service2 Front-end Service registry User Front-end Service registry Micro-service1 Micro-service2
- 9. 9 Application Performance Monitoring Micro- service1 Micro- service2 Front-end Service registry User Front-end Service registry Micro-service1 Micro-service2
- 10. 10 Application Performance Monitoring Micro- service1 Micro- service2 Front-end Service registry User Front-end Service registry Micro-service1 Micro-service2
- 11. 11 Operational profile workload probability 0.0 0.4 0.3 0.2 0.1 operational profile 50 100 150 200
- 12. 12 PPTAM: Overview (container diagram) PPTAM record performance tests pass/fail outcome Test Engineer performs qualiy assurance, release management Product manager visualize tries to isolate problems Software developer APM Tools Dashboard Driver Testbed retrieves Analysis Architect Repository looks for architectural alternatives reads to generate tests plan stores test results deploys/ queries
- 13. 13 PPTAM: Overview (component diagram) PPTAM Driver Testbed Docker swarm Test-by-test configuration Test orchestrator drives SUT describes SUT configuration calls queries stores test results Testing framework Metrics collection Load test template uses stores configures PPTAM Configuration uses Analytics Extraction/ Aggregation of SUT Data Repository visualize reads from reads Test plan generator System logs Dashboarding 0 1 2 3 4 5 Test Engineer Product manager Software developer Architect reads to generate test plan APM Tools writes uses runs reads deployed on looks for architectural alternatives record performance tests pass/fail outcome performs qualiy assurance, release management tries to isolate problems
- 14. 14 PPTAM: Process t workload 9 am Observe workload situations workload rel. freq. Empirical distribution of workload situations sampled workload rel. freq. Sample selection workload p(w) 50 100 150 .11 .19 .22 ... ... Load test sequence Analysis Sampling Experiment generation Experiment execution Testbed Test engine PPTAM service metric 1 2 ... ... ... ... Baseline requirements Load test template Architectural alternatives Results for architectural alternatives 123 1 2 3 4
- 15. 15 Analysis of the results response time workload Baseline workload
- 16. 16 Analysis of the results response time workload Baseline workload mean response time for baseline service 1
- 17. 17 Analysis of the results response time workload Baseline workload mean response time for baseline service 1
- 18. 18 Analysis of the results response time workload Baseline workload mean response time for baseline service 1
- 19. 19 Analysis of the results response time workload Baseline workload mean response time for baseline service 1
- 20. 20 Analysis of the results response time workload Baseline workload mean response time for baseline service 1
- 21. 21 Analysis of the results response time workload Baseline workload Maximum tolerated workload for service 1 mean response time for baseline service 1
- 22. 22 Analysis of the results response time workload Baseline workload Maximum tolerated workload for service 1 mean response time for baseline service 2 Maximum tolerated workload for service 2 mean response time for baseline service 1
- 23. 23 Analysis of the results response time workload Baseline workload Maximum tolerated workload for service 1 mean response time for baseline service 2 Maximum tolerated workload for service 2 mean response time for baseline service 1 Operating point
- 24. 23 Analysis of the results response time workload Baseline workload Maximum tolerated workload for service 1 mean response time for baseline service 2 Maximum tolerated workload for service 2 mean response time for baseline service 1 Operating point Variable Service 1 Service 2 x(l0) 0.018 2.008 σ 0.008 0.003 Req. 0.042 2.017 x(lop) 0.015 2.009 Pass/fail pass pass Calls 20% 80%
- 25. 23 Analysis of the results response time workload Baseline workload Maximum tolerated workload for service 1 mean response time for baseline service 2 Maximum tolerated workload for service 2 mean response time for baseline service 1 Operating point Variable Service 1 Service 2 x(l0) 0.018 2.008 σ 0.008 0.003 Req. 0.042 2.017 x(lop) 0.015 2.009 Pass/fail pass pass Calls 20% 80% Success rate=20% + 80%
- 26. 24 Analysis of the results response time workload Baseline workload Maximum tolerated workload for service 1 mean response time for baseline service 2 Maximum tolerated workload for service 2 mean response time for baseline service 1 Operating point Variable Service 1 Service 2 x(l0) 0.018 2.008 σ 0.008 0.003 Req. 0.042 2.017 x(lop) 2.015 2.009 Pass/fail fail pass Calls 22% 78% Success rate=78%
- 27. 25 Success rate for different workloads sampled workload situation success rate 0.0 1 0.5 50 100 150 200 architectural alternative 2 architectural alternative 1
- 28. 26 Operational profile workload probability 0.0 0.4 0.3 0.2 0.1 operational profile 50 100 150 200
- 29. 27 Success rate × probabilty sampled workload situation domain metric 0.0 0.4 0.3 0.2 0.1 operational profile architectural alternative 1 architectural alternative 2 50 100 150 200
- 30. 28 Ongoing work: identification of Antipatterns – Application Hiccups: temporarily increased response times that return to normal later. – The Stifle: data is retrieved through many similar (or equal) database queries. As each request causes a considerable overhead, the high amount of database requests leads to a performance problem. – Traffic Jam: one problem causes a backlog of jobs that produces wide variability in response time which persists long a er the problem has disappeared.
- 31. 29 Thank you for your attention!