CERN Data Analytics Use Cases

1. CERN Data Analytics Use Cases Manuel Martin Marquez Stefano Alberto Russo

2. Today’s Objectives • Overview - Data Analytics at CERN • Use Cases • Context • Status • Technologies applied • Limitations • Future Plans and Challenges • Real Time Analytics • Batch • Analytics Repository • AaaS - Analytics as a Service Manuel Martin Marquez – CERN openlab

3. Overview: Data Analytics at CERN Manuel Martin Marquez – CERN openlab

4. Overview: Data Analytics at CERN • Huge interest and potential benefits for different domains at CERN • IT – Information Technology • BE – Beams • EN – Engineering • PH – Physics • No one size fits all • Real time, batch, data management. • Different solutions, technologies, approaches Manuel Martin Marquez – CERN openlab

5. Use Cases: Accelerator Data Analysis • Context • Logging Service persist several million signals • Core infrastructure: Electricity, • Industrial Data: Cryogenics, Vacuum, • Beam data: position, currents, losses, • Critical Service • About 800 extraction clients • About 120 custom applications • More than 5 million request per day • Massive increase (2-3x) in 2014 • Extensive use of Oracle Technologies Manuel Martin Marquez – CERN openlab

6. Use Cases: Accelerator Data Analysis • Current Status: • Data analysis focused on the service itself • Improving reaction time and performance • Basic streaming analysis: • Complex Event Processing • Based on system knowledge • Basic in-database analysis • built-in (PL/SQL) • Embeded in the extraction tool • Complex cases pushed to the users • Matlab, Python, Labview, etc. Manuel Martin Marquez – CERN openlab

7. Use Cases: Accelerator Data Analysis • Future Plans and Challenges: • Extract more value from the data • Focus on accelerator operations • Make common data analysis use cases easier • Save and share analysis results • Simple and fast access to data • Domain specific language • Not replacing the existing infrastructure and tools Manuel Martin Marquez – CERN openlab

8. Use Cases: Industrial Control Systems Analytics • Context: • Support for Large industrial control systems • Five major installations with million of parameters • ALICE • ATLAS • CMS • LHb • Accelerator Complex • Many Equiment groups • Cryo • Gas • Vacuum • Machine Protection Manuel Martin Marquez – CERN openlab

9. Use Cases: Industrial Control Systems Analytics • Current Status: • Monitoring and control problem solved • Alerting and reporting system • Manually configured • Based on threshold • Huge data volumen acquiere and stored • OS logs, performances metrics, device status, Measurements, Alarms • Not much efficiently exploited Manuel Martin Marquez – CERN openlab

10. Use Cases: Industrial Control Systems Analytics • Current Status: • P.O.C already started: • Control System Health • Alarm statistical Analysis • Gas System Breakdown • Evaluation of different technologies • Drools • WatchCAT • Facing Problems: • Data Access (Sensible and Protected) • Integration of different data sources • Common Data Analysis problems: classification, completeness, Manuel Martin Marquez – CERN openlab

11. Use Cases: Industrial Control Systems Analytics • Future Plans and Challenges: • AaaS: Common framework for all the subsystems • Configurable analysis flow by user • High scalability of analysis processes • Near real time and batch analysis • Stream based data processing engine: CEP, Storm • NoSQLdata storage engine Manuel Martin Marquez – CERN openlab

12. Use Cases: Atlas Distributed DM System Analytics • Context: • The Distributed Data Management Project (DDM) manage ATLAS data on the GRID • 150 PB • 1000 Active Users • 500 million files Manuel Martin Marquez – CERN openlab

13. Use Cases: Atlas Distributed DM System Analytics • Current Status: • Use both SQL and NoSLQ • NoSQL complemetary to RDBMS • Different uses cases • Popularity analysis • Data Aggregation and statistical analysis Manuel Martin Marquez – CERN openlab

14. Use Cases: Atlas Distributed DM System Analytics • Future Plans and Challenges: • More complex use cases • Trace Mining • Analysis of the client interactions • Replicas automatically managed • Deletion • Creation • Forecasting • Future dataset popularity Manuel Martin Marquez – CERN openlab

15. Use Cases: Intelligent data placement for CMS • Context • CMS Grid resources for storage and offline analysis • Hundreds users • Daily up to 500.000 jobs • Data sample replicated • 23 PB of data • 18 PB transfer last year Manuel Martin Marquez – CERN openlab

16. Use Cases: Intelligent data placement for CMS • Current Status: • Current Data Management model • Manpower intensive • Inneficient disk usage • Data Popularity Services • Cleaning Agent • Automatic Deletion of obsolete replicas • Implented using Oracle DB • Metrics (Number of accesses, users, sites, processing time, etc) • Multiple aggregations (Jobs success/failure, set of files, etc) Manuel Martin Marquez – CERN openlab

17. Use Cases: Intelligent data placement for CMS • Future Plans and Challenges: • LHC Run 2 implies a 6x factor in computing resources • Critical to optimize resources • Jobs time in accessing analysing data • Minimizing number of replicas • Extract further knowledge from Monitoring data • Classify analysis activities and predict resources • Recommendation systems • Learn from past trends and patters Manuel Martin Marquez – CERN openlab

18. Use Cases: Intelligent data placement for CMS • Future Plans and Challenges: • Near real time • Based on knowledge extracted from the data (Bacth) • CEP • Batch analysis • Different technologies • R • In-database analytics - Oracle R Entreprise • Hadoop • Elastic Search Manuel Martin Marquez – CERN openlab

19. Use Cases: Network Monitoring WLCG • Context: • WLCG relies heavily on the underlying networks • Interconnect sites and resources • PerfSONAR - Network Perfomance Measurement and Monitoring Manuel Martin Marquez – CERN openlab

20. Use Cases: Network Monitoring WLCG • Current Status: • PerfSONAR deployed on 70% of infrastructure • A Lot of data but making sense out of it not trivial at all • Measurements span different time periods • They measure different things (while all related to network) • They might be affected by other measurements and/or events Manuel Martin Marquez – CERN openlab

21. Use Cases: Network Monitoring WLCG • Future Plan and Challenges: • From Monitoring to Intelligent & Predicting Monitoring • Time correlation • During a PS throughput test, was there any known activity in the same link? • There is packet loss, does this appears as degraded performance somewhere at the same time • Loss of performance • Is it a network problem and where? • Is it a storage problem? • Analyze the existing data, mine the information looking for known issues in the past Manuel Martin Marquez – CERN openlab

22. Use Cases: IT Monitoring and Analytics • Context: • Monitoring in IT covers a wide range of resources • Hardware, OS, applications, files, jobs, etc. • Many high level resources are interdependent • Several application-specific monitoring solutions • Similar needs and architecture • Publish metric results, aggregate results, alarms, etc. • Different technologies and tool-chains • Some based on commercial solutions • Similar limitations and problems • Limited sharing of monitoring data Manuel Martin Marquez – CERN openlab

23. Use Cases: IT Monitoring and Analytics • Current Status: • Data Storage and Analysis • Store all monitoring data in a common location • Feed the system with processed data • Use one single common data format (JSON) • Permanent storage for historical data • Data Visualization and Alarms • Easy-to-use dashboards • Efficient delivery of notifications Manuel Martin Marquez – CERN openlab

24. Use Cases: IT Monitoring and Analytics • Future Plan and Challenges: • Intelligent and Predictive Monitoring • Real time analytics • Dashboard and interactive analytics • Batch analysis: Data mining – exploratory • AaaS based on several technologies • Hadoop • Elastic Search • Kibana • Storm Manuel Martin Marquez – CERN openlab

25. Use Cases: Analytics in Castor • Context: The CERN Advanced Storage Manager (CASTOR) is the mass storage solution of CERN, including LHC data • hierarchical storage, both disks and tape • 12K disks, 30K tapes, • More than 100 PB of data • lot of monitoring/log data • up to 20 GB per day (~100M lines of log) • totaling ~10 TB per year. • stored in Hadoop/HBase and • processed live for display in a cockpit Manuel Martin Marquez – CERN openlab

26. Use Cases: Analytics in Castor • Current Status: Monitoring system in production for CASTOR and being extended to EOS. • online (simple) analysis in the Cockpit • (timeseries and histograms) • offline analyses on the long-term storage based on Hadoop/HBase. • auditing, • error recovery • historical studies (e.g. usage of protocols) • Service availability being registered on the Service Level Status (SLS) board Manuel Martin Marquez – CERN openlab

27. Use Cases: Analytics in Castor • Future Plan and Challenges: The current system is not covering two important topics: • Expert system: spotting "interesting" time series out of a large monitoring data • Early warning system: find predictive power to forecast potential dangerous situations Investigation of these topics started together with the DB group as part of the openlab data analytics activity • finding the best models is challenging • lot of data from different sources (avoiding time-consuming eye inspection) (e.g. overload conditions!) Data Analytics DB SLS Cockpit (standardization required!) Manuel Martin Marquez – CERN openlab

28. Analytics as a Service (AaaS) • Real-time data analysis • Complex Event Processing • Pattern Recognition • Streaming data analysis • Batch data analytics • Forecasting • Modelling • Knowledge discovery for later apply to real time Manuel Martin Marquez – CERN openlab

29. Analytics as a Service (AaaS) • Data analytics repository • Flexible data repository infrastructure • Problem driven – no technology driven • A combination of RDMS and noSQL • Integrating existing data sources and systems • Analytics framework (AaaS) • Real-time analytics • Batch • Data analytics repo • Data Analytics Visualization Manuel Martin Marquez – CERN openlab

30. Conclusions • Huge interest and potential benefits for CERN • IT, BE, PH, EN departments • Improve our Monitoring and control systems by mean of Data Analytics • Intelligent • Proactive • Predictive Manuel Martin Marquez – CERN openlab

31. Conclusions • Challenges • Real time analytics based on CERN use case • Based on domain knowledge and hidden knowledge extracted by batch analytics • CEP, Storm • Batch analytics • Correlation analysis • Forecasting modeling • Knowledge discovering • Data analytics repository • AaaS Manuel Martin Marquez – CERN openlab