1. CompSci 296.2 Self-Managing Systems Shivnath Babu

2. 2 Today Project schedule (reminder) Finish QueS presentation –System, challenges Sample projects If we have time, start ROC discussion

3. 3 Project Group size <= 2 Identify “general topic” by end of January, meet Shivnath Feb 7: Scope the problem, give 15-minute talk Feb 21: 3-minute talk March 7: 15-minute talk March 28: 3-minute talk April 4/6: 15-minute talk April 20/24: 15-minute final in-class presentation (+ “demo”)

4. 4 Querying Systems as Data What are probable causes of the Service-Level-Agreement (SLA) violations rising to 12%? Root-cause query

5. 5 Queries: What if … Given today’s workload, how will average response time change if my database fails? If I double the memory on my application servers, how will SLA violation rate change?

6. 6 Queries: Let me know … Let me know if, with 75% probability, average response time will exceed 5 seconds in next 30 minutes –Prediction –Continuous query

7. 7 Queries: What should I do? What should I do to reduce SLA violations of requests A to <1%, without increasing violations of other requests? –Root-cause + What-if

8. 8 Querying Systems as Data Instrumented traces, logs System activity data Data from active probing Workload System configuration data (e.g., buffer size, indexes) Source code Models –Analytic performance models –Machine learning models –Rules from system experts –Simulators DATADATA

9. 9 Querying Systems with QueS (30,000 ft) DATADATA Query Processor Data Acquisition Data Maintenance Model- driven DB Engine Queries Answers System mgmt. services

10. 10 Challenges: Query Complexity Support for complex queries –Rank probable causes of SLA violation rising to 12%? –“What should I do” queries Queries may be acquisitional

11. 11 Challenges: Query Specification Declarative query language –Expressibility of language –Composition Snapshot queries and continuous queries

12. 12 Challenges: Query Processing Model-based query processing Many types of data sources –Structured, semi-structured, and unstructured Uncertainty in input data –E.g., legacy systems may have partial/no instrumentation Imprecise answers –Answers may include quantification of accuracy –Ranking

13. 13 Challenges: Run-time Overhead Real-time service for 24x7 systems Tunable data acquisition Active probing

14. 14 Sample Projects NIMO Fa What-if querying for database systems Combining structured & unstructured data Projects using Nagios Projects using IBM software

15. 15 Sample Project (in progress) NIMO (Piyush Shivam) Answering queries about: 1.Expected performance given a resource assignment 2.Feasible resource assignments to meet SLA 3.What-if queries for applications in network utilities

16. 16 Sample Project (in progress) Fa (Songyun Duan) Can we automate problem-prediction and diagnosis? Use of Bayesian Networks for: Predicting performance problems (continuous query) Root-cause queries

17. 17 Sample Project What-if queries on database configuration- parameter settings –Ex: What happens to transaction response times if I change value of parameter X from v to v’

18. 18 Sample Project Combined querying of structured and unstructured system data –Structured data: MySQL performance counters, processor utilization, number of I/O accesses –Unstructured data: Application and system logs Interested: Hao He

19. 19 Sample Project Add problem-prediction capability to Nagios Add root-cause querying to Nagios Similar projects using the IBM Autonomic Computing Toolkit + ABLE framework –Ex: Wrap them inside a query interface

20. 20 Projects at HP Research Project 1: Predicting performance problems, finding root causes of problems Project 2: Debugging complex systems Project 3: Designing adaptive systems (using control theory)