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Improving System Availability in Distributed Environments

Improving System Availability in Distributed Environments. Sam Malek malek@usc.edu with Marija Mikic-Rakic marija@usc.edu Nels Beckman beckman@usc.edu Nenad Medvidovic neno@usc.edu. Motivation. How good is this deployment architecture?. What are its properties?.

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Improving System Availability in Distributed Environments

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  1. Improving System Availability in Distributed Environments Sam Malekmalek@usc.edu with Marija Mikic-Rakic marija@usc.edu Nels Beckman beckman@usc.edu Nenad Medvidovic 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 • 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 310 = 59049 possible deployments Redeployment Better deployment  Higher availability Bad deployment  Low availability

  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 • 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 • Exponentially complex problem • n components and k hosts = kn possible deployments • DeSi’s polynomial time approximating algorithms • Solution analysis • Comparison of different solutions and algorithms • Centralized vs. Decentralized, performance vs. complexity, etc • DeSi’s visualization and comparison utilities • Effecting the selected solution • Redeploying components • Requires an automated solution • Prism-MW deployment support

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

  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 DeSi 2) Monitoring Data 4) Redeployment Data 3) Analyze 1) Monitor Prism-MW’s Role • 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. Prism-MW DeSi 2) Monitoring Data 4) Redeployment Data 3) Analyze 1) Monitor DeSi’s Role • 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?

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