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GRID monitoring

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GRID monitoring Gennaro Tortone (INFN Napoli) [gennaro.tortone_at_na.infn.it] GRID.it meeting Bologna 14.2.2003 Summary monitoring of Grid elements GLUE schema EDG ... – PowerPoint PPT presentation

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Title: GRID monitoring


1
GRID monitoring
  • Gennaro Tortone (INFN Napoli) gennaro.tortone_at_na
    .infn.it

GRID.it meeting Bologna 14.2.2003
2
Summary
  • monitoring of Grid elements
  • GLUE schema
  • EDG-WP4 monitoring framework
  • tasks
  • future activities

3
Monitoring of grid elements (1/2)
Computing Element
Resource Broker
Storage Element
Worker Node
Worker Node
Worker Node
Information Index
Replica Catalog
Worker Node
Replica Manager
  • LOW LEVEL measurements
  • CPU load
  • memory usage
  • disk usage (per partition)
  • network activity
  • number of processes
  • number of users (UI)
  • SERVICE checks
  • gatekeeper
  • gsiftp
  • gris
  • gdmp
  • RB/LB
  • GRID measurements
  • number of total CPUs
  • number of free CPUs
  • number of running jobs
  • number of waiting jobs
  • SE free disk space

4
Monitoring of grid elements (2/2)
  • sources of information
  • LOW LEVEL measurements -gt plugins/sensors
    installed on each machine
  • SERVICE checks -gt sensors installed on monitoring
    server
  • GRID measurements -gt sensors installed on
    monitoring server
  • aggregate information
  • per VO
  • per site

5
GLUE schema
  • Conceptual model of grid resources to be used as
    a base schema of the GIS(Grid Information
    Service) for discovery and monitoring purposes
  • model of computing resources (CE)
  • model of storage resources (SE)
  • model of relationships among them (close CE/SE)
  • Implementation status (v. 1.0) (for Globus MDS)
  • LDAP schema (DataTAG WP4.1)
  • information providers (CE/SE)
  • GLUE schema extension to include all monitoring
    metricsdone host level added to GLUE schema

6
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7
EDG-WP4 monitoring framework
  • It provides a client (Monitoring Sensor Agent -
    MSA) running sensors (Monitoring Sensors - MS) on
    each node to monitor, and a central server
    (Fabric Monitoring Server - fmonServer) to
    collect data.
  • The server receives samples as they are measured
    by MSA, and stores them in a flat file / Oracle
    database
  • The client is provided with a sensor
    (sensorLinuxProc) which uses /proc file system to
    measure various basic quantities on Linux (CPU
    load, network, etc).

8
EDG-WP4 monitoring framework
local farm element
computing element
9
ldap query
information index
ldap query
monitoring server
write
run
ldif output
farm monitoringarchive
read
computing element
10
Tasks (1/3)
  • identify the requirements for Grid monitoring
  • done Grid monitoring analysis draft with some
    LCG inputs(available on http//gridmon.na.infn.i
    t/lcg-edt)
  • evaluation of existing monitoring tools (sensors)
    to use as first monitoring layer on each
    grid-element
  • done tools evaluated
  • EDG-WP4 fabric-monitoring tool (fmon)
  • client-server model
  • very easy to use
  • very easy to install (one RPM without
    dependencies)
  • highly customizable (time interval for each
    metric, )
  • it is very easy to add a new metric
  • historical archive
  • database in Oracle/plain-text format

11
Tasks (2/3)
  • extension of the WP4 fabric-monitoring tool
    (fmon) to include other monitoring metrics
  • done (all metrics added are available on
    http//gridmon.na.infn.it/lcg-edt)
  • GLUE schema extension to include all monitoring
    metrics
  • done host level added to GLUE schema
  • development of information-providers to fill
    the GLUE host level extension done
  • definition of database structure to store
    snapshot/historical monitoring data done

12
Tasks (3/3)
  • automatic resource discovery using MDS
    infrastructure and GLUE schema in progress
  • development of a web interface to display various
    grid-views (per VO, per site, etc.) in
    progress

13
Future activities
  • personal Grid-monitoring integration with
    VOMS
  • job monitoring
  • evaluation of OGSA as monitoring service
  • development of a Grid monitoring tool
  • scalability
  • very low intrusivity
  • automatic resource discovery
  • fault detection and notification
  • metrics graphs
  • web interface

14
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15
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