1. Improving System Availability in Distributed Environments Sam Malek malek@usc.edu malek@usc.edu with Marija Mikic-Rakic marija@usc.edu marija@usc.edu Nels Beckman beckman@usc.edu beckman@usc.edu Nenad Medvidovic neno@usc.edu neno@usc.edu

2. Motivation How good is this deployment architecture? What are its properties? How should it be modified to ensure higher availability?

3. Effect of Deployment on Availability Bad deployment  Low availability Better deployment  Higher availability Redeployment Redeployment to maximize the availability –Frequency and volume of interactions, reliability and capacity of network links Hard to determine a good deployment in large scale distributed systems –In the small example above, there are 3 10 = 59049 possible deployments

4. Availability Definition The degree to which the system is operational and accessible when required for use

5. System Model Parameters Software component properties Memory requirements Frequency of interaction Size of the exchanged data Hardware host properties Memory capacity Network reliability Network bandwidth Constraints Location Co-location

6. Problem Definition Find a system deployment architecture such that: It adheres to the system model parameters and constraints It has the greatest availability

7. Problem Break Down 1)Lack of knowledge about runtime system parameters –System model parameters not known at the time of initial deployment –System model parameters change at runtime Reliability of links, frequencies of interaction, etc. –Prism-MW monitoring support 2) Exponentially complex problem –n components and k hosts = k n possible deployments –DeSi’s polynomial time approximating algorithms 3) Solution analysis –Comparison of different solutions and algorithms –Centralized vs. Decentralized, performance vs. complexity, etc –DeSi’s visualization and comparison utilities 4) Effecting the selected solution –Redeploying components –Requires an automated solution –Prism-MW deployment support

8. DeSi Approach Prism-MW 2) Monitoring Data 1) Monitor 4) Redeployment Data 3) Analyze

9. Prism-MW –An architectural middleware that enables efficient implementation, deployment, and execution of distributed systems in terms of their architectural elements: components, connectors, configurations, etc. –Support for monitoring –Support for redeployment Simplified Class Diagram of Prism-MW

10. Prism-MW’s Role DeSi Prism-MW 2) Monitoring Data 1) Monitor 4) Redeployment Data 3) Analyze Supports: Step 1 by monitoring events in the system and calculating the system parameters Step 4 by providing an API for the redeployment of components and meta-level components to automate the tasks

11. Maximizing Availability A family of centralized algorithms Exact – exponential Stochastic – quadratic Adaptive greedy – cubic A family of decentralized algorithms DecAp: Auction-based – cubic A set of clustering techniques –Reduce complexity –Improve performance

12. Algorithms’ Results

13. Assessing the Algorithms Efficiency –Execution time vs. precision Applicability –Centralized vs. Decentralized Effect of system characteristics Impact of individual parameter changes Addition of new system parameters Application to new system properties Requires “what if” scenario exploration In comes DeSi!

14. DeSi’s Architecture Key properties: Tailorability Scalability Efficiency Explorability

15. DeSi’s View (1)

16. DeSi’s View (2)

17. DeSi’s View (3)

18. DeSi’s View (4)

19. DeSi’s View (5)

20. DeSi’s Role DeSi Prism-MW 2) Monitoring Data 1) Monitor 4) Redeployment Data 3) Analyze Supports: Step 3 by providing several redeployment algorithms and various visualization utilities Steps 2 and 4 by providing the appropriate middleware adapter

21. Conclusion Suite of automated tools and techniques for improving the availability of a distributed system Currently extending the tools to model, analyze, and improve other non-functional aspects of a distributed system: security, latency, etc.

22. Questions?