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Computational Grids and Computational Economy: Nimrod/G Approach

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Title: Computational Grids and Computational Economy: Nimrod/G Approach


1
Computational Grids and Computational Economy
Nimrod/G Approach
David Abramson Rajkumar Buyya Jonathan Giddy
2
Parametric Execution of Applications
  • Coarse-grained SPMD model
  • Execute one application repeatedly for many
    combinations of input parameters
  • Legacy applications add iteration and
    distribution without modifying code
  • New applications remove iteration and
    distribution from design
  • Parametrised modeling experiments
  • Require very high levels of performance
  • Generate
  • Large amounts of work concurrency
  • Uncoupled computations
  • Tolerate - moderately high latencies

3
Description of Parameters
4
Working with Small Clusters
  • Nimrod (1994 - )
  • DSTC funded project
  • Designed for department level clusters
  • Proof of concept
  • Clustor (Activetools) (1997 - )
  • Commercial version of Nimrod
  • Re-engineered
  • Features
  • Workstation orientation
  • Access to idle workstations
  • Random allocation policy
  • Password security

5
Clustor limitations
  • Manual resource location
  • static file of machine names
  • No resource scheduling
  • first come first served
  • No cost model
  • all machines cost alike
  • Single access mechanism

6
Towards Grid Computing.
Source www.globus.org updated
7
Nimrod/G - Nimrod over Globus/Grid
  • Wide-Area Network Support
  • redesigned architecture
  • use of high-performance networks
  • Scalable Scheduling
  • guaranteed deadline
  • use of existing schedulers
  • Computational Economy
  • I am willing to pay , can you complete the job
    by given deadline
  • trading, bidding, resource reservation...

8
Layered Architecture (Grid Components)
Applications
High-level Services and Tools
GlobusView
Testbed Status
Nimrod/G
DUROC
globusrun
MPI
MPI-IO
CC
Core Services
GRAM
Nexus
Metacomputing Directory Service
Globus Security Interface
Heartbeat Monitor
Gloperf
GASS
Local Services
Condor
MPI
TCP
UDP
LSF
NQE
Easy
Solaris
Irix
AIX
Source www.globus.org
9
Nimrod/G Architecture
Nimrod/G Client
Nimrod/G Client
Nimrod/G Client
Parametric Engine
Schedule Advisor
Resource Discovery
Persistent Info.
Dispatcher
Grid Directory Services
Grid Middleware Services
GUSTO Test Bed
10
Nimrod/G Interactions
  • Additional services used implicitly
  • GSI (authentication authorization)
  • Nexus (communication)

Resource location
MDS server
Scheduler
Resource allocation (local)
Prmtc.. Engine
Queuing System
Job Wrapper
Dispatcher
GRAM server
Root node
Gatekeeper node
Computational node
11
Scheduling Algorithm
  • Find a set of machines (MDS search)
  • Distribute jobs from root to machines
  • Establish job consumption rate for each machine
  • For each machine
  • Can we meet deadline?
  • If not, then return some jobs to root
  • If yes, distribute more jobs to resource
  • If cannot meet deadline with current resource
  • Find additional resources

12
A Nimrod/G Client
13
Sample Applications of Nimrod
  • Bioinformatics Protein Modeling
  • Sensitivity experiments on smog formation
  • Parametric study of Laser detuning
  • Combinatorial Optimization Simulated Annealing
  • Ecological Modeling Control Strategies for
    Cattle Tick
  • Electronic CAD Field Programmable Gate Arrays
  • Computer Graphics Ray Tracing
  • High Energy Physics Searching for Rare Events
  • Physics Laser-Atom Collisions
  • VLSI Design SPICE Simulations
  • Radiation Protection and Nuclear Safety

14
Electronic CAD
15
Some early results -
16
(No Transcript)
17
Related Works
  • AppLeS (UC. San Diego)
  • application level scheduling case-by-case
  • NetSolve (UTK/ORNL)
  • API for creating farms
  • DISCWorld (U. Adelaide)
  • remote information access
  • Millennium (UC. Berkeley)
  • remote execution environment on clusters and
    supports computational economy

18
Conclusions
  • Nimrod/G architecture offers a scalable model
    for resource management and scheduling on
    computational grids
  • Supports Computational Economy
  • The current model supporting Parametric
    Computing can be extended to support parallel
    jobs or any other computational model.
  • Plan to use the concept of Advance Resource
    Reservation in order to offer the feature wherein
    the user can say I am willing to pay , can you
    complete my job by this time
  • Further Information www.csse.monash.edu.au/david
    a/nimrod.html
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