Scheduling Architecture and Algorithms within ICENI Laurie Young, Stephen McGough, Steven Newhouse, John Darlington London e-Science Centre Department of Computing, Imperial College London

Contents ICENI –Scheduling Architecture Scheduling Algorithms –Variety of different algorithms Experimental Results –Different policies –Different Grid sizes –Different Application Profiles

ICENI The Iceni, under Queen Boudicca, united the tribes of South-East England in a revolt against the occupying Roman forces in AD60. IC e-Science Networked Infrastructure Developed by LeSC Grid Middleware Group Collect and provide relevant Grid meta-data Use to define and develop higher-level services Interaction with other frameworks: OGSA, Jxta etc.

Component Applications Factory Design Generator Analyser Mesh Generator DRACS Mesh Generator Mesh Generator DRACS Each job is composed of multiple components. Each runs on a different resource Each component is connected to at least one other component. Data is passed along these connections

ICENI Scheduling Architecture ICENI Launching Framework Condor Launcher Globus Launcher Scheduling Framework Simulated Annealing Game Theory Schedule Evaluation Performance Repository Performance Model Statistical Prediction ICENI Scheduling Services Launching Framework Pluggable Launchers (SGE, Globus, Condor, ICENI) Scheduling Framework Pluggable Schedulers (Simulated Annealing, Game Theory Random, Best of n Random) Performance Framework Pluggable Performance Repositories (Perf. Models, Statistical Analysis)

Use a Benefit Function. Also called a Utility Function or Evaluation Function. A Benefit Function maps the metrics we are interested in to a single Benefit Value. Different benefit functions represent different optimisation preferences. Can set benefit to 0 if constraints (e.g. Budget) exceeded. Schedule Evaluation

Random / Best of n Random Random Best of n Random Random Scheduler Randomly selects a schedule Checks schedule can be executed Produces schedules very quickly Best of n Random Produces multiple random schedules Returns the best one Still very fast Better results than the random schedules

Simulated Annealing Monte Carlo method Generate schedule at random Modify current schedule Accept new schedule if better –If worse, accept with probability proportional to “temperature” and inversely proportional to benefit change Repeat, while reducing “temperature” Stop when no modifications to schedule accepted Random Simulated Annealing

Each component is a “Player” Each player has to choose best strategy (Grid resource) Each strategy has a benefit, depending on the strategy chosen by all other players. Players identify, then remove strategies guaranteed to never be optimal – “strictly dominated strategies” Produces the “Nash Equilibrium” Game Theory Player B 1234 Player A 1 6,36,47,38,4 2 4,73,94,75,6 3 4,55,57,46,5 4 7,45,56,47,6

Schedulers Random / Best of n Random Produces usable schedules fast. Game Theory Considers the scheduling problem as an economic problem. Simulated Annealing Algorithm for solving optimisation Problems Scheduling Policy Time Optimisation Best benefit from a schedule with the shortest execution time. Results show scheduling time + execution time. Cost Optimisation Best benefit from a schedule where the cost of using resources is low. Grid Description 4 Clusters of resources Saturn 16 Sparc III 750 MHz Processors 5Gbit Interconnects Rhea 8 Sparc III 900Mhz Processors 5Gbit Interconnects Viking T 16 node, 2GHz Pentium 4 1Gbit Interconnects Viking C 16 node, 2GHz Pentium 4 100Mbit Interconnects Simulated Scheduling Framework Consistent Interface Uses the same interface as the ICENI scheduling framework allowing the same schedule code to be used. Repeatability As the underlying description files never change the same experiment can be run many times. Application Description 21 DAG Applications Varying Depth DAG Depth between 2 and 7 Varying Complexity Between 2 and 8 Components Average component would take 2 minutes on an 2Ghz CPU Average communication between components would take 1 minute on a 100Mbit network Experiments Scheduler Simulated Scheduling Framework Grid and Application Description Files

Results (Cost Optimisation)

Results (Time Optimisation)

Summary ICENI Scheduling Architecture –Comprised of 3 services, using a pluggable architecture to allow different implementations to be used –Launcher implementations allow launching to different underlying execution environments. –Performance service enables execution time predictions –Scheduling service operates on information provided by other two services Decouples scheduler from application and environment

Summary Scheduling Algorithms –Four algorithms examined while varying: Grid Sizes Applications Policies –Simulated Annealing generally the best algorithm tested –Larger applications take longer to schedule and return –More choice in resources leads to: cheaper computation for users Longer return times for applications Increasing the Grid size can reduce or improve the quality of service experienced by the user

Acknowledgements Director: Professor John Darlington Technical Director: Dr Steven Newhouse Research Staff: –Anthony Mayer, Nathalie Furmento –Stephen McGough, James Stanton –Yong Xie, William Lee –Marko Krznaric, Murtaza Gulamali –Asif Saleem, Laurie Young, Gary Kong Contact:: – – Funding: –PPARC e-Science Studentship (PPA/S/E/2001/03335)