1. Solving Manufacturing Equipment Monitoring Through Efficient Complex Event Processing Tilmann Rabl, Kaiwen Zhang, Mohammad Sadoghi, Navneet Kumar Pandey, Aakash Nigam, Chen Wang, Hans-Arno Jacobsen Middleware Systems Research Group, University of Toronto DEBS Grand Challenge 2012

2. Agenda Complex Event Processing Scenarios System Architecture Evaluation Demo 18/07/20122msrg.org

3. Motivation Course Large-Scale Data Management Big data storage Large scale event processing Complex event processing scenarios Application performance management Smart traffic monitoring Energy monitoring Resulting team project DEBS Grand Challenge 18/07/20123msrg.org

4. Scenario I: Application Performance Management Monitoring of enterprise systems Find bottlenecks, problems Trace transactions, measure utilization 18/07/20124msrg.org

5. Scenario II: Smart Traffic Monitoring Traffic data from cars, mobile devices, road sensors Event aggregation, filtering, correlation Traffic status, accident detection, etc. 18/07/20125msrg.org

6. Scenario III: Energy Monitoring Green computing Application-level energy monitoring API-based energy consumption estimation Operating System & Hardware Applications CPU Network I/O Formulae Sensors … Store API 18/07/20126msrg.org

7. Common Denominators High data rates 1000 – 1000000+ events / sec Small data points < 1 KB Complex queries Filtering, aggregation, correlation Persistent storage Distributed setup DEBS Grand Challenge 18/07/20127msrg.org

8. Continuous Query Evaluation High-Level Architecture Monitoring Service Input data stream, marshalling Event Dissemination Substrate (Optional) pub/sub layer, queues Continuous Query Evaluation Consumes input, computes results Storage Manager Stores data, enables querying Client Visualizes query results Java-based implementation Monitoring Service Monitoring Service Storage Manager Storage Manager Client Event Dissemination Substrate Stable Storage 18/07/20128msrg.org

9. Storage Architecture Data is stored in tables Key-value pairs Table Index Fast lookup Compressor Efficient data storage Run length encoding Grand Challenge data Run-length: 20000 Compression: 99.99% 18/07/20129msrg.org

10. Client Google Web Toolkit-based Client Java-code compiled to JavaScript Displays all Grand Challenge results Plots, results, alarms 18/07/201210msrg.org

11. Evaluation Distributed setup Separated servers for data generator and monitoring tool Configuration 2 servers 2 x dual core Xeon processor 4 GB RAM Gigabit Ethernet Data set: 5 min + 18 days + synthetic Synthetic data Many errors (every 2 - 3 min) Maximum throughput (no network) Metrics: latency & throughput 18/07/201211msrg.org

12. Processing Overhead Real Workload 0 – 200 queries (Q1,Q2 repeated) Linear in the number of queries Stable with increasing generator speedup 18/07/201212msrg.org

13. Throughput Real Workload Throughput controlled by data generator Data generator does not saturate system Peak 9000 events/sec ~ 0.11 ms arrival rate Well below maximum throughput 18/07/201213msrg.org

14. Latency Synthetic Workload Maximum throughput Minimum latency 0.019 ms (10 queries) Maximum latency 0.63 ms (1400 queries) 18/07/201214msrg.org

15. Throughput Synthetic Workload Maximum throughput 40000 events / sec (20 queries) Minimum throughput 1500 events / sec (1400 queries) 18/07/201215msrg.org

16. Demo Live! 18/07/201216msrg.org

17. Conclusions Complex event processing scenarios Application performance management Smart traffic monitoring Energy monitoring DEBS Grand Challenge Efficient implementation of the Grand Challenge Java-based Google Web Toolkit GUI Synthetic data generator Up to 40000 events per second Up to 1400 queries 18/07/201217msrg.org

18. Thank You! Questions? Contact: Tilmann Rabl tilmann.rabl@utoronto.ca msrg.org 18/07/201218msrg.org

19. Back-Up: DEBS Grand Challenge Manufacturing equipment monitoring Large, real data set 18 days, 100 Hz 2 queries State of sensors and valves Difference Threshold Power consumption Range Average Threshold 18/07/201219msrg.org