ORCAS: Efficient Resilience Benchmarking of Microservice Architectures André van Hoorn Aldeida Aleti Thomas F. Düllmann Teerat Pitakrat 9th Symposium on Software Performance (SSP 2018) November 08, 2018. Hildesheim
Previously Presented at ISSRE 2018 André van Hoorn, Aldeida Aleti, Thomas F. Düllmann, Teerat Pitakrat: ORCAS: Efficient Resilience Benchmarking of Microservice Architectures. ISSRE 2018
“ Recurring solution to common problem with Resilience Antipattern negative consequences for the system” (Brown et al. Antipatterns: Refactoring Software, Architectures, and Projects in Crisis. John Wiley & Sons, Inc., 1998) M1 E1 M2 DB E2 M3 Integration Points Cascading Failures Slow Responses van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 3
Resilience Pattern Example: Circuit Breaker M1 E1 Circuit Breaker M2 DB E2 M3 van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 4
Resilience Benchmarking – aka Chaos Engineering • How to accept failures? – Learning by doing: Intentionally inject failures into the production system “Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production .” — Principles of Chaos • Who is doing this? Game Day exercises Simian Army for AWS van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 5
„ “ Current resilience benchmarking practice is inefficient.” André et al. Goal: Make it more efficient! van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 6
Idea of the Project Leverage relationship between resilience patterns, antipatterns, and fault injections Consider software architectural knowledge to generate experiments Combine model-based (simulations) and measurement-based („real“) resilience experiments van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 7
Envisioned Framework Static and dynamic analysis + manual enrichment extraction Architectural System Information • Services, deployment, (remote) interactions • Patterns and anti-patterns • Criticality of services • Steady-state metrics van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 8
Envisioned Framework Static and dynamic analysis + manual enrichment extraction input Orcas Decision Architectural System Engine Information Knowledge and algorithms – „ the magic “ • Services, deployment, (remote) interactions • Patterns and anti-patterns • Criticality of services • Steady-state metrics van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 9
Envisioned Framework extraction input Orcas Decision Architectural System Engine Information Experiment real execution generation Workload Faultload results van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 10
Envisioned Framework extraction input Orcas Decision Architectural System Engine Information Experiment real execution generation Workload Faultload input results Experiment sim input System generation Workload Simulation Faultload results van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 11
Current and Next Steps • PoC implementation • Evaluation of injection frameworks • Simulator extensions • Developing „ the magic “ • Experimental evaluation • Industry case study (?) van Hoorn et al.: Efficient Resilience Benchmarking of Microservice Architectures 12
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