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Performance Architecture within ICENI

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Title: Performance Architecture within ICENI


1
Performance Architecture within ICENI
  • Dr Andrew Stephen McGough
  • Laurie Young, Ali Afzal, Steven Newhouse and John
    Darlington
  • London e-Science Centre
  • Department of Computing, Imperial College London

2
Outline
  • Overview of ICENI
  • Performance Framework
  • Example
  • Conclusion

3
ICENI Imperial College e-Science Network
Infrastructure
The Iceni, under Queen Boudicca, united the
tribes of South-East England in a revolt against
the occupying Roman forces in AD60.
4
Scheduling Workflows in ICENI
  • Applications consist of a number of components
    linked together in a dataflow manner
  • The abstract workflow needs to be mapped down to
    a set of component implementations which will run
    on resources

Linear Equation Source
Linear Equation Solver
Display Vector Results
5
The Problem
  • We have
  • Multiple resources where components can run
  • Multiple implementations of components
  • The choice of one resource component mapping can
    affect the others
  • User wants predictable performance
  • How to choose the best mapping of workflow over
    resources to give user predictability?

6
The Solution
  • We need to take into account
  • Execution Times of components on Resources
  • Performance Data
  • Inter-component effects of workflows
  • Workflow aware Schedulers
  • Workload on resources, making sure they are free
    when we need them
  • Reservation systems

7
The Architecture
Workflow
Application Service
Scheduler
Launcher
8
The Performance Repository Framework
9
Collection of Performance Results
Linear Equation Source
Linear Equation Solver
Time Event 1200
Linear Equation Source Start 1204
Send out Equations 1203
Linear Equation Solver Start 1205
Receive Equations 1212
..
Display Vector Results
10
Storing Performance Data
  • Multiple stores can be used in one framework
  • The stores may be data stores or analytical
    models
  • All assumed to be persistent
  • Allows requests for predictions to be made
  • New Data can be added to the stores
  • Store data is aggregated together
  • based upon reliability of store data
  • Provided by the store

11
Using Performance Data
  • Scheduler Builds up workflow graph with timings
    requested from the Performance Repository
  • Timings are based on component implementation,
    resource, co-allocation count and other
    properties defined by the component implementer
  • As the store will not contain all possible
    combinations of these properties regression is
    used to provide estimates for the missing values
  • This is an area of ongoing research

12
Reservation Engine
Reservation Engine Framework
  • Listen out for requests
  • Launcher services wishing to makereservations

13
Reservation Service
Reservation Service Framework
  • Listen out for Services
  • Launcher with reservation
  • Scheduling Services

14
Reservations
Reserve (workflow)
Linear Equation Source
Linear Equation Solver
Display Vector Results
WS-Agreement Request interval
time ?
time ?
Reservations not possible on Users Desktop
15
Example Linear Equation Solver
service list
composition pane
parameters
16
Inferring Workflow from Dataflow
17
Conclusion
  • Better usage of resources.
  • Reservations of resources in the future
  • Determining if co-allocation of components will
    affect performance
  • Late Enactment of components
  • Critical Path analysis can schedule this
    appropriately
  • Provides a framework for experimentation with
  • Different scheduling algorithms
  • Different performance models
  • Different reservation policies

18
Performance Trinity

Performance Models
Grid Scheduling
Reservation Fabric
19
The Architecture Showing the Trinity
Reservation Service
Scheduler
Application Service
Reservation Engine
Performance Store
Launcher
20
Acknowledgements
  • Director Professor John Darlington
  • Research Staff
  • Nathalie Furmento, Stephen McGough, William Lee
  • Jeremy Cohen, Marko Krznaric, Murtaza Gulamali
  • Asif Saleem, Laurie Young, Jeffrey Hau
  • David McBride, Ali Afzal
  • Support Staff
  • Oliver Jevons, Sue Brookes, Glynn Cunin, Keith
    Sephton
  • Alumni
  • Steven Newhouse, Yong Xie, Gary Kong
  • James Stanton, Anthony Mayer, Angela OBrien
  • Contact
  • http//www.lesc.ic.ac.uk/ ? e-mail
    lesc_at_ic.ac.uk

21
Scheduling Architecture in ICENI
Globus Resources
Globus Launcher
Submit
Advertise
Software Resource Repository
Single Resource Launcher
Scheduler
Query
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