The Grid Observatory: towards collaborations between EGEE and CoreGRID

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The Grid Observatorytowards collaborations
between EGEE and CoreGRID

• C. Germain-Renaud
• Laboratoire de Recherche en Informatique
• Laboratoire Accélérateur Linéaire
• CNRS Université Paris-Sud XI
• Desden 9-11 May 2007

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The EGEE Grid

• FP6 and FP7 infrastructure
• Large-scale, production-quality grid
  infrastructure for e-Science
• 91 partners, 32 countries
• 30K CPUs, 10PB,
• 30K jobs
• 24X7

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Applications

• Applications from a growing number of domains
• High Energy Physics
• Life Sciences
• Astrophysics
• Computational Chemistry
• Earth Sciences
• Financial Simulation
• Fusion
• Geophysics
• Multimedia
• Material Sciences

Applications have moved from testing to routine
and daily usage
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Collaborating e-infrastructures
Potential for linking 80 countries by 2008
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Sustainability

• Need to prepare for permanent Grid infrastructure
• Ensure a high quality of service for all user
  communities
• Independent of short project funding cycles
• Infrastructure managed in collaboration with
  National Grid Initiatives (NGIs)

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The Grid Observatory - overview

• Part of the EGEE-III proposal (NA4 Cluster)
• LRI, LAL, London Imperial College, Trinity
  College Dublin, Università Piemonte Orientale
• Integrate the collection of data on the behaviour
  of the EGEE grid and users with the development
  of models and of an ontology for the domain
  knowledge.

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Data Collection and Publication

• Design, implement and deploy a set of tools for
  the acquisition, consolidation, long-term
  conservation, and publication of traces of EGEE
  activities
• Permanent storage of reliable, exhaustive,
  filtered information
• Publication service
• Navigation and structured access
• Requires indexing along the needs of the
  scientific communities
• Operational basis for collaboration with national
  research initiatives and other UE projects
• Input for Machine Learning research e.g. PASCAL
  (Pattern Analysis Statistical Modelling and
  Computational Learning) Challenge, or KD-Ubiq
  summer School 2008
• Input for research in grid architecture and
  middleware
• Baseline references

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Data Collection and Publication

• Scientific Challenges
• Most data is organized along operational
  semantics redundant and often undocumented
  features
• A Grid Ontology would be required for efficient
  indexing and querying. The glue schema is an
  ontology of the grid components. It is a good
  starting point, but we need concepts for the grid
  dynamics (e.g. job lifecycle or users relation
  graph), and for performance metrics
• Lack of usage-oriented sensors at the network
  level

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Data Collection and Publication

• Technical challenges
• Ecosystem of sources with very different scopes,
  deployment and institutional status CIC tools
  (GOCDB, SAM, SFT), core gLite (LB, BDII,),
  sites (Maui/PBS logs), gLite integrators (R-GMA,
  Job Provenance), experiences integrators
  (DashBoard), external software (Alice/MonaLisa)
• Volume, operational constraints
• Legal challenge
• Privacy
• Scientific usage helps, but to be considered from
  the beginning

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Example operational monitoring tools (From Ian
Birds talk _at_EGEE06)

• Tools used by the Grid Operator on Duty team to
  detect problems
• Distributed responsibility
• CIC portal
• single entry point
• Integrated view of monitoring tools
• Site Functional Tests (SFT) -gt Service
  Availability Monitoring (SAM)
• Grid Operations Centre Core Database (GOCDB)
• GIIS monitor (Gstat)
• GOC certificate lifetime
• GOC job monitor
• Others

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The Logging and Bookkeeping

• Behavioural trace of a job lifecycle users,
  middleware and infrastructure
• LAL Broker 2005-2006 300K jobs, 3Mevts, 2GB

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Pre-processing the LB logs

• A lot of information in blobs incremental
  verbatim of the reports from the various services
  
• More than 400 attributes

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Models

• Models are required for dimensioning, capacity
  planning, optimizing grid middleware and
  applications
• Self-aware grids behavioural models
• Intrinsic characterizations of grid traffic
  (distribution of) e.g. jobs running time,
  application data locality
• Characterizations of middleware-dependant metrics
  e.g. queuing delays, SE load
• Inference of models for middleware components,
  applications (including, but not limited to,
  workflows), users and usage profiles

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Evaluation

• Assessing performance at the grid scale is a
  challenge
• Need a snapshot of the inputs and grid state e.g.
  workload and available services during a relevant
  time range
• Classical optimization does not scale
• Advanced optimization anytime algorithms
• Experiments in controlled settings

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Autonomic grids

• Autonomic computing computing systems that
  manage themselves in accordance with high-level
  objectives from humans
• Self- configuration, healing, optimization,
  protection
• Industrial (IBM, Bouygues) and academic (IEEE
  conference since 2004, ECML tutorial 2006,)
  activity
• Machine learning techniques
• Relevant for grids
• The components are known to be complex systems
• Very large datasets, but actually sparse in the
  state space
• Non steady-state dynamic behavior
• High computational (analysis) of operational
  (probing) costs

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Autonomic grids

• Autonomic computing computing systems that
  manage themselves in accordance with high-level
  objectives from humans
• Self- configuration, healing, optimization,
  protection
• Industrial (IBM, Bouygues) and academic (IEEE
  conference since 2004, ECML tutorial 2006,)
  activity
• Machine learning techniques
• Techniques
• Pruning data feature selection and construction
• Identifying the main modes of the grid
  supervised (classification, regression,
  collaborative prediction) and unsupervised
  (clustering,,) learning
• On-line decision making active learning,
  reinforcement learning

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Autonomic dependability

• On-line failure detection and anticipation
• Passive vs Active probing a lot of information
  is available from useful work
• Black-box
• On-line statistics from  similar  actions
  (executions, data access, middleware modules)
  sequential testing
• Off-line supervised and unsupervised learning
• Active probing
• Adaptive on-line test selection for best coverage
  of possibly faulty components
• Experience planning

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Example blackhole detection
Software bug

• Site fault
• Page-Hinckley statistics
• Time-sequential version of Walds statistics
  also known as CUSUM
• Provides an  intelligent threshold  test

Blackhole
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Example Mining the Logging and Bookkeeping data

• Aggressive sub-sampling
• Constructive feature induction
• SVM and ROGER (ROC based genetic learner)
• Double clustering (Slonim Tishby 2000) with
  K-means
• first clustering compress the features along
  the examples
• second clustering cluster the examples along the
  synthetic features
• Stable clusters
• Good prediction performance when ABU excluded
• Mostly pure clusters, natural use for detection,
  diagnostic in progress

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Other areas for interaction

• Differentiated services without advance
  reservation
• Urgent, but not fully explicit, requirement
  priorities (analysis), hard real-time (critical),
  soft real-time (interactive), latency (isolated
  job), bandwidth,
• Two EGEE WG
• SDJ (Short Deadline Jobs) based on controlled
  time-sharing
• Priorities based on refinements of VO (role) and
  CE (VOView)
• Necessarily short-term solutions, but useful for
  exploring constraints and basic support
  mechanisms
• Basic research no quantum, faire share is at the
  accounting time scale
• Working with gLite
• A turn-key deployment of gLite on Xen is planned

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Conclusion

• The Grid Observatory targets
• Contributions to a quantitative approach of grid
  middleware and architecture, in the RISC sense,
  through data collection, publication and
  characterization
• Statistically significant benchmarks
• Basic research in autonomic computing
• Operational impacts on EGEE evaluation,
  autonomic dependability
• Advances in grid ontology
• The Grid Observatory data and analysis are
  relevant for computer science, middleware
  development and system administration, well
  beyond EGEE