Grid Monitoring Futures with Globus

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Grid Monitoring Futures with Globus

• Jennifer M. Schopf
• Argonne National Lab
• April 2003

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My Definitions

• Grid
• Shared resources
• Coordinated problem solving
• Multiple sites (multiple institutions)
• Monitoring
• Discovery
• Registry service
• Contains descriptions of data that is available
• Expression of data
• Access to sensors, archives, etc.

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What do I mean by Grid monitoring?

• Different levels of monitoring needed
• Application specific
• Node level
• Cluster/site Level
• Grid level
• Grid level monitoring concerns data
• Shared between administrative domains
• For use by multiple people
• (think scalability)

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Grid Monitoring Does Not Include

• All the data about every node of every site
• Years of utilization logs to use for planning
  next hardware purchase
• Low-level application progress details for a
  single user
• Application debugging data (except perhaps
  notification of a failure of a heartbeat)
• Point-to-point sharing of all data over all sites

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Overview of This Talk

• Evaluation of information infrastructures
• Globus Toolkit MDS2, R-GMA, Hawkeye
• Insights into performance issues
• (publication at HPDC 2003)
• What monitoring and discovery could be
• Next-generation information architecture
• Open Grid Services Architecture mechanisms
• Integrated monitoring discovery arch for GT3

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Performance and the Grid

• Its not enough to use the Grid, it has to
  perform otherwise, why bother?
• First prototypes rarely consider performance
  (tradeoff with devt time)
• MDS1centralized LDAP
• MDS2decentralized LDAP
• MDS3decentralized Grid service
• Often performance is simply not known

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Globus Monitoring andDiscovery Service (MDS2)

• Part of Globus Toolkit, compatible with other
  elements
• Used most often for resource selection
• aid user/agent to identify host(s) on which to
  run an application
• Standard mechanism for publishing and discovery
• Decentralized, hierarchical structure
• Soft-state protocols
• Caching
• Grid Security Infrastructure credentials

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MDS2 Architecture
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Relational Grid Monitoring Architecture (R-GMA)

• Implementation of the Grid Monitoring
  Architecture (GMA) defined within the Global Grid
  Forum (GGF)
• Three components
• Consumers
• Producers
• Registry
• GMA as defined currently does not specify the
  protocols or the underlying data model to be
  used.

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GGF Grid Monitoring Architecture
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R-GMA

• Monitoring used in the EU Datagrid Project
• Steve Fisher, RAL, and James Magowan, IBM-UK
• Based on the relational data model
• Used Java Servlet technologies
• Focus on notification of events
• User can subscribe to a flow of data with
  specific properties directly from a data source

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R-GMA Architecture
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Hawkeye

• Developed by Condor Group
• Focus automatic problem detection
• Underlying infrastructure builds on the Condor
  and ClassAd technologies
• Condor ClassAd Language to identify resources in
  a pool
• ClassAd Matchmaking to execute jobs based on
  attribute values of resources to identify
  problems in a pool

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Hawkeye Architecture
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Comparing Information Systems
 
 
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Some Architecture Considerations

• Similar functional components
• Grid-wide for MDS2, R-GMA Pool for Hawkeye
• Global schema
• Different use cases will lead to different
  strengths
• GIIS for decentralized registry no standard
  protocol to distribute multiple R-GMA registries
• R-GMA meant for streaming data currently used
  for NW data Hawkeye and MDS2 for single queries
• Push vs Pull
• MDS2 is PULL only
• R-GMA allows push and pull
• Hawkeye allows triggers push model

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Experiments

• How many users can query an information server at
  a time?
• How many users can query a directory server?
• How does an information server scale with the
  amount of data in it?
• How does an aggregator scale with the number of
  information servers registered to it?

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Testbed

• Lucky cluster at Argonne
• 7 nodes, each has two 1133 MHz Intel PIII CPUs
  (with a 512 KB cache) and 512 MB main memory
• Users simulated at the UC nodes
• 20 P3 Linux nodes, mostly 1.1 GHz
• R-GMA has an issue with the shared file system,
  so we also simulated users on Lucky nodes
• All figures are 10 minute averages
• Queries happening with a one second wait between
  each query (think synchronous send with a 1
  second wait)

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Metrics

• Throughput
• Number of requests processed per second
• Response time
• Average amount of time (in sec) to handle a
  request
• Load
• percentage of CPU cycles spent in user mode and
  system mode, recorded by Ganglia
• High when running small number compute intensive
  aps
• Load1
• average number of processes in the ready queue
  waiting to run, 1 minute average, from Ganglia
• High when large number of aps blocking on I/O

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Performance of Information Servers vs. Number of
Users
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Experiment 1 Summary

• Caching can significantly improve performance of
  the information server
• Particularly desirable if one wishes the server
  to scale well with an increasing number of users
• When setting up an information server, care
  should be taken to make sure the server is on a
  well-connected machine
• Network behavior plays a larger role than
  expected
• If this is not an option, thought should be given
  to duplicating the server if more than 200 users
  are expected to query it

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Directory Server Scalability
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Experiment 2 Summary

• Because of the network contention issues, the
  placement of a directory server on a highly
  connected machine will play a large role in the
  scalability as the number of users grows
• Significant loads are seen even with only a few
  users, it will be important that this service be
  run on a dedicated machine, or that it be
  duplicated as the number of users grows.

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Information Service Throughput vs. Num. of
Information Collectors
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Experiment 3 Summary

• Too many information collectors is a performance
  bottleneck
• Caching data helps
• Alternatively, register to more instances of
  information servers with each handling a subset
  of the collectors

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Overall Results

• Performance can be a matter of deployment
• Effect of background load
• Effect of network bandwidth
• Performance can be affected by underlying
  infrastructure
• LDAP/Java strengths and weaknesses
• Performance can be improved using standard
  techniques
• Caching multi-threading etc.

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So what could monitoring be?

• Basic functionality
• Push and pull (subscription and notification)
• Aggregation and Caching
• More information available
• More higher-level services
• Triggers like Hawkeye
• Viz of archive data like Ganglia
• Plug and Play
• Well defined protocols, interfaces and schemas
• Performance considerations
• Easy searching
• Keep load off of clients

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Topics

• Evaluation of information infrastructures
• Globus Toolkit MDS2, RGMA, Hawkeye
• Throughput, response time, load
• Insights into performance issues
• What monitoring and discovery could be
• Next-generation information architecture
• Open Grid Services Architecture mechanisms
• Integrated monitoring discovery arch for GT3

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Open Grid Services Architecture (OGSA)

• Defines standard interfaces and behaviors for
  distributed system integration, especially
• Standard XML-based service information model
• Standard interfaces for push and pull mode access
  to service data
• Notification and subscription

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Key OGSI concept - serviceData

• Every service has its own service data
• OGSA has common mechanism to expose a service
  instances state data to service requestors for
  query, update and change notification
• Monitoring data is baked right in
• Service-level concept, not host-level concept

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serviceData

• Every Grid Service can expose internal state as
  serviceData elements
• An XML element of arbitrary complexity
• Each service has a serviceData set
• The collection of serviceData Elements (SDEs)
• Example state of a host is exposed as an SDE by
  GRAM.
• Similar to MDS2 GRIS functionality, but in each
  service (rather than once per host)

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ExampleReliable File Transfer Service
File Transfer
Internal State
Data transfer operations
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MDS3 Monitoring and Discovery System

• Consists of a various components
• Core functionality
• Information providers
• Higher level services
• Clients

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Core Functionality

• Xpath support
• XPath is a language that describes a way to
  locate and process items in XML docs by using an
  addressing syntax based on a path through the
  document's logical structure or hierarchy
• Xindice support native XML database
• Registry support

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Schema Issues

• Need to keep track of service data schema
• Avoid conflicts
• Find the data easier
• Should really have unified naming approach
• All of the tool are schema-agnostic, but
  interoperability needs a well-understood common
  language

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MDS3 Information Providers in June Release

• All the data currently in core MDS2
• Full data in the GLUE schema for compute elements
  (CE)
• Ganglia information provider for cluster data
  will also be available from Ganglia folks (with
  luck)
• Service data from RFT, RLS, GRAM
• GT2 to GT3 work
• GridFTP server data
• Software version and path data
• Documentation for translating your GT2
  information provider to a GT3 information provider

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MDS3 Higher Level Products

• Higher-level services can perform actions on
  service data collected from other services
• Part of this functionality can be provided by a
  set of building blocks provided
• Provider interface GRIS-style API for writing
  information providers
• Service Data Aggregator set up subscriptions to
  data for other services, and publish it as a
  single data stream
• Hierarchy Builder allow for hierarchy of
  aggregators

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MDS3 Index Server

• Simplest higher-level service is the caching
  index service
• Much like the GIIS in MDS2
• Will have configurablity like an GIIS hierarchy
• Will also have PHP-style scripts, much as
  available today

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Clients currently in GT3

• findServiceData command line client
• Same functionality of grid-info-search
• C bindings
• Core C bindings provide findServiceData C
  function
• findServiceData command line client gives an
  example of using it to parse out information (in
  this case, registry contents)

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Service Data Browser

• GUI client to display service data from any
  service
• Extensible for data-specific visualization
• A version was released with GT3 alpha 
• http//www.globus.org/ogsa/releases/
  alpha/docs/infosvcs/sdbquickstart.html

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Comparing Information Systems
 
 
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Is this enough?

• No!
• Many places where additional help developing MDS3
  is needed

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We Need More Basic Information

• Interfaces to other sources of data
• GPT data
• Other monitoring systems
• Others?
• Service data from other components
• Every service has service data
• OGSA-DAI
• Will need to interface on schema

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We Will Need More GUIs and Clients

• Additional GUI visualizers may be implemented to
  display service data specific to a particular
  port type (as part of service data browser)
• Additional Client interfaces possibly
• Integration into current portals, brokers

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We Need MoreHigher Level Services

• We have a couple planned
• Archiving service
• Trigger template

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Post-3.0 release Archiving Service

• Will allow subscription to service data
• Logging in a flexible way
• Well defined interfaces for mining
• Open questions
• Best way to store time-series of arbitrary XML?
• Best way to query this archive?
• Link to OGSA-DAI?
• Link to other archivers?

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Post-3.0 release Trigger Template

• Will provide a template to allow subscription to
  data, reasoning about that data, and a course of
  action to take place
• Essentially, a gateway service between OGSA
  Notifications and some other notification
  framework, with filtering of notifications
• Example Subscribe to disk space information,
  send mail to sys admin when it reached 90 full
• Needed trigger template and several small
  examples of common triggers, and documentation
  for how users could extend them or write new
  ones.

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Other Possible HigherLevel Services

• Site Validation Service
• Job Tracking Service
• Interfacing to Netlogger?

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We Need Security

• Need I say more?

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Summary

• Current monitoring systems
• Insights into performance issues
• What we really want for monitoring and discovery
  is a combination of all the current systems
• Next-generation information architecture
• Open Grid Services Architecture mechanisms
• MDS3 plans
• Additional work needed!

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Thanks

• Testbed/Experiment support and comments
• John Mcgee, ISI James Magowan, IBM-UK Alain Roy
  and Nick LeRoy at University of Wisconsin,
  MadisonScott Gose and Charles Bacon, ANL Steve
  Fisher, RAL Brian Tierney and Dan Gunter, LBNL.
• This work was supported in part by the
  Mathematical, Information, and Computational
  Sciences Division subprogram of the Office of
  Advanced Scientific Computing Research, U.S.
  Department of Energy, under contract
  W-31-109-Eng-38. This work also supported by
  DOESG SciDAC Grant, iVDGL from NSF, and others.

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Additional Information

• MDS3 technology coordinators
• Ben Clifford (benc_at_isi.edu)
• Jennifer Schopf (jms_at_mcs.anl.gov)
• Zhang, Freschl and Schopf, A Performance Study
  of Monitoring and Information Services for
  Distributed Systems, to appear in HPDC 2003
• http//people.cs.uchicago.edu/hai/hpdcv25.doc
• MDS-3 information
• Soon at www.globus.org/mds

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Extra Slides
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Why Information Infrastructure?

• Distributed, often complex, performance-critical
  nature of Grids apps demands tools for
• Discovering available resources
• Discovering available sensors
• Integrating information from multiple sources
• Archiving and replaying historical information
• These and other functions are provided by an
  information infrastructure
• Many projects are concerned with design,
  deployment, evaluation, and application

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Performance of GIS Information Servers vs. Number
of Users
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Performance of GIS Information Servers vs. Number
of Users
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Performance of GIS Information Servers vs. Number
of Users
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Performance of GIS Information Servers vs. Number
of Users
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Directory Server Scalability
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Directory Server Scalability
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Directory Server Scalability
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Directory Server Scalability