The Agile Infrastructure Project. Monitoring. Markus Schulz Pedro Andrade. CERN IT Department CH-1211 Genève 23 Switzerland

Transcription

1 The Agile Infrastructure Project Monitoring Markus Schulz Pedro Andrade CERN IT Department CH-1211 Genève 23 Switzerland

2 Outline Monitoring WG and AI Today s Monitoring in IT Architecture Vision Implementation Plan Conclusions 2

3 Monitoring WG and AI Markus Schulz 3

4 Introduction Motivation Several independent monitoring activities in IT similar overall approach, different tool-chains, similar limitations High level services are interdependent combination of data from different groups necessary, but difficult Understanding performance became more important requires more combined data and complex analysis Move to a virtualized dynamic infrastructure comes with complex new requirements on monitoring Challenges Find a shared architecture and tool-chain components while preserving our investment in monitoring IT Monitoring Working Group 4

5 Timeline Q Creation of Monitoring WG and mandate definition Presentations of monitoring status per IT group Q Presentations of monitoring plans per IT group Initial discussion on a shared monitoring architecture Q Definition of common tools and core user stories Agreement on a shared monitoring architecture Q Preparation of MWG summary report Definition of implementation plans in the context on AI Q Setup of infrastructure and prototype work. Import data from several sources into the Analysis Facility. Exercise messaging at expected rates and feed the storage system. 5

6 Today s Monitoring in IT Pedro Andrade 6

7 Monitoring Applications Group CF CIS Lemon, LAS, SLS CDS, Indico Applications CS DB DI DSS ES GT OIS PES Spectrum CA Events, Polling Value, Alarm History, Performance Analysis, Sflow/Nflow, Syslog, Wireless Monitoring Database monitoring, Web applications monitoring, Infrastructure Monitoring Central Security Logging All, Central Security Logging Logins, IP connections log, Deep Packet Inspection, DNS Logs TSM, AFS, CASTOR Tape, CASTOR Stager Job Monitoring, Site Status Board, DDM Monitoring, Data Popularity, Hammer Cloud, Frontier, Coral SAM-Nagios SCOM Job Accounting, Fairshare, Job Monitoring, Real-time Job Status, Process Accounting 7

8 Monitoring Applications 8

9 Monitoring Data Producers Input Volume 283 GB per day Input Rate 697 M entries per min 2,4 M entries per min without PES/process accounting Query Rate 52 M queries per day 3,3 M entries per day without PES/process accounting 9

10 Analysis Monitoring in IT covers a wide range of resources Hardware, OS, applications, files, jobs, etc Many application-specific monitoring solutions Some are commercial solutions Based on different technologies Limited sharing of monitoring data Maybe no sharing, simply duplication of monitoring data All monitoring applications have similar needs Publish metric results, aggregate results, alarms, etc 10

11 Architecture Vision Pedro Andrade 11

12 Constraints (Data) Large data store aggregating all monitoring data for storage and combined analysis tasks Make monitoring data easy to access by everyone! Not forgetting possible security constraints Select a simple and well supported data format Monitoring payload to be schema free Rely on a centralized metadata service(s) to discover the computer center resources information Which is the physical node running virtual machine A Which is the virtual machine running service B Which is the network link used by node C this is becoming more dynamic in the AI 12

13 Constraints (Technology) Focus on providing well established solutions for each layer of the monitoring architecture Transport, storage, analysis Flexible architecture where a particular technology can be easily replaced by a better one Adopt whenever possible existing tools and avoid home grown solutions Follow a tool chain approach Allow a phased transition where existing applications are gradually integrated 13

14 User Stories User stories were collected from all IT groups and commonalities between them were identified To guarantee that different types of user stories were provided three categories were established: Fast and Furious (FF) Get metrics values for hardware and selected services Raise alarms according to appropriate thresholds Digging Deep (DD) Curation of hardware and network historical data Analysis and statistics on batch job and network data Correlate and Combine (CC) Correlation between usage, hardware, and services Correlation between job status and grid status 14

15 Architecture Overview Splunk Portal Report Alarm Portal Hadoop Oracle Analysis Application Specific Storage Storage Feed Alarm Feed Custom Feed Apollo Aggregation Lemon Publisher Sensor Publisher Sensor 15

16 Architecture Overview All components can be changed easily Including the messaging system (standard protocol) Messaging and storage as central components Tools connect either to the Messaging or Storage Publishers should be kept as simple as possible Data produced either directly on sensor or after a first level of aggregation Scalability can be addressed either by horizontally scaling or by adding additional layers Pre-aggregation, pre-processing Fractal approach 16

17 Data Format The selected message format is JSON A simple common schema must be defined to guarantee cross-reference between the data. Timestamp Hardware and node Service and applications Payload These base elements (tag) require the availability of the metadata service(s) mentioned before This is still under discussion 17

18 Messaging Broker Two technologies have been identified as the best candidates: Apollo and RabbitMQ Apollo is the successor ActiveMQ Prior positive experience in IT and the experiments Only realistic testing environments can produce reliable performance numbers. The use case of each application must be clear defined Total number of producers and consumers Size of the monitoring message Rate of the monitoring message The trailblazer applications have already very demanding use cases 18

19 Central Storage and Analysis All data is stored in a common location Makes easy the sharing of monitoring data Promotes sharing of analysis tools Allows feeding into the system data already processed NoSQL technologies are the most suitable solutions Focus on column/tabular and document based solutions Hadoop (from the Cloudera distribution) as first step 19

20 Central Storage and Analysis Hadoop is a good candidate to start with Prior positive experience in IT and the experiments Map-reduce paradigm is a good match for the use cases Has been used successfully at scale Many different NoSQL solutions use Hadoop as backend Many tools provide export and import interfaces Several related modules available (Hive, HBase) Document based store also considered CouchDB/MongoDB are good candidates For some use cases a parallel relational database solution (based on Oracle) could be considered 20

21 Messaging Integrating Closed Solutions External (commercial) monitoring Windows SCOM, Oracle EM Grid Control, Spectrum CA These data sources must be integrated Injecting final results into the messaging layer Exporting relevant data at an intermediate stage Visualization/Repor ts Analysis Storage Transport Sensor Export Interface Integrated Product 21

22 Implementation Plan Pedro Andrade 22

23 Transition Plan Moving the existing production monitoring services to a new base architecture is a complex task as these services must be continuously running A transition plan was defined and foresees a staged approach where the existing applications gradually incorporate elements of the new architecture 23

24 Transition Plan Portal Report Alarm Analysis NEW Storage Storage Feed Alarm Feed Aggregation OLD Publisher Publisher Publisher 24

25 Milestones Monitoring.v1 Q AI nodes monitored with Lemon (dependency on Quattor) Deployment of Messaging Broker and Hadoop cluster Testing of other technologies (Splunk) Monitoring.v2 Q AI nodes monitored with Lemon (no dependency on Quattor) Lemon data starts to be published via messaging Monitoring.v3 Q Several clients exploiting the messaging infrastructure Messaging consumers for real time alarms and notifications Initial data store/analysis for select use cases Monitoring.v4 Q Monitoring data published to the messaging infrastructure Large scale data store/analysis on Hadoop cluster 25

26 Monitoring v1 Several meetings organized einfradocsminutes Short-term tasks identified and tickets created Work ongoing on four main areas: Messaging broker deployment Hadoop cluster deployment Testing of Splunk with Lemon data Lemon agents running on puppet 26

27 Monitoring v1 Deployment of the messaging broker Based on Apollo and RabbitMQ Three SL6 nodes have been provided 2 nodes for production, 1 node for development Each node will run Apollo and RabbitMQ Three applications have been identified to start using/testing the messaging infrastructure OpenStack MCollective Lemon 27

28 Monitoring v1 Testing Splunk with Lemon data Lemon data to be exported from DB (1 day, 1 metric) Data exported into a JSON file and stored n AFS This data will be imported to Splunk Splunk functionality and scalability will be tested Started the deployment of a Hadoop cluster Taking the Cloudera distribution Other tools may also be deployed (HBase, Hive, etc) Hadoop testing using Lemon data (as above) is planned 28

29 Monitoring v1/v2 AI nodes monitored with existing Lemon metrics First step Current Lemon sensors/metrics are used for AI nodes Lemon metadata will still be taken from Quattor A solution is defined to get CDB equivalent data Second step Current Lemon sensors/metrics are used for AI nodes Lemon metadata is not taken from Quattor Lemon agents start using the messaging infrastructure 29

30 Conclusions Pedro Andrade 30

31 Conclusions A monitoring architecture has been defined Promotes sharing of monitoring data between apps Based on few core components (transport, storage, etc) Several existing external technologies identified A concrete implementation plan has been identified It assures a smooth transition for today s applications It enables the new AI nodes to be monitored quickly It allows moving towards a common system 31

32 Links Monitoring WG Twiki (new location!) Monitoring WG Report (ongoing) ngreport Agile Infrastructure TWiki Agile Infrastructure JIRA 32

33 Thanks! QUESTIONS? 33