slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Shikha Mehrotra Centre for Development of Advanced Computing CDAC, Bangalore, India PowerPoint Presentation
Download Presentation
Shikha Mehrotra Centre for Development of Advanced Computing CDAC, Bangalore, India

Loading in 2 Seconds...

play fullscreen
1 / 20

Shikha Mehrotra Centre for Development of Advanced Computing CDAC, Bangalore, India - PowerPoint PPT Presentation


  • 94 Views
  • Uploaded on

Shikha Mehrotra Centre for Development of Advanced Computing CDAC, Bangalore, India {shikham@cdac.in}. AN INGENIOUS APPROACH FOR IMPROVING TURNAROUND TIME OF GRID JOBS WITH RESOURCE ASSURANCE AND ALLOCATION MECHANISM. Outline. Indian National grid GARUDA Need for Reservation in Grid

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Shikha Mehrotra Centre for Development of Advanced Computing CDAC, Bangalore, India' - walter


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
ShikhaMehrotra

Centre for Development of Advanced Computing

CDAC, Bangalore, India

{shikham@cdac.in}

AN INGENIOUS APPROACH FOR IMPROVING TURNAROUND TIME OF GRID JOBS WITHRESOURCE ASSURANCE AND ALLOCATION MECHANISM

IEEE HPEC'12

outline
Outline
  • Indian National grid GARUDA
  • Need for Reservation in Grid
  • Approach followed in realizing reservation in Garuda Grid
    • Architecture
    • Features
  • Performance analysis
    • Job flow in Garuda grid
    • Performance metrics
    • Turnaround time of grid jobs
    • Case-study
      • Turn-around time without reservation
      • Turn-around time with reservation
      • Data analysis
      • Results
  • Conclusion

IEEE HPEC'12

grid computing
Grid Computing
  • Distributed Computing taken to the next level
  • Aggregation of Resources from many participants (geographically distributed in general)
    • Compute resources
    • Data resources
    • Special instruments (Telescopes, microscopes, so on..)
  • Unified, Seamless access to these resources
    • Analogous to the “Power Grid”

IEEE HPEC'12

india s national grid computing initiative garuda
India’s National Grid Computing Initiative: GARUDA
  • Motivation
    • To Collaborate on Research and Engineering of Technologies, Architectures, Standards and Applications in Grid Computing
    • To Contribute to the aggregation of resources in the Grid
  • Currently
    • Connects more than 60 institutions
      • Academic & Research labs
    • Spans across 17 cities of India
    • Supports 10 Virtual Organizations
      • Bioinformatics, Seismic engineering, Climate modeling, Drug discovery ….
  • Production infrastructure with
    • Gigabit networking backbone (NKN)
    • Large HPC computing resources
    • Massive Storage
    • Tools and Services for Unified Access

IEEE HPEC'12

problem statement
Problem Statement
  • As the demand for the resources increases more and more, it becomes really difficult to manage the jobs and allocate resources to them and hence most of the jobs will be in the queued state waiting for the resource to be free.

IEEE HPEC'12

our approach
Our Approach
  • Reduce waiting time
  • Solution : Advance Reservation of resources
    • An advance reservation is a reservation that a user or administrator can request and the scheduler can create.
    • It guarantees the availability of resources at specified future time slot.

IEEE HPEC'12

compute reservation
Compute Reservation
  • An advance reservation is essentially defined by the following:
    • Start time which is defined using the standard date-time format
    • An end time, which is either defined using the standard date-time format or computed from the start time plus a duration value,
    • Number and type of resource to be reserved.

IEEE HPEC'12

garuda reservation architecture

APPLICATIONS

COMMANDS

API

GARUDA GRID LEVEL RESERVATION COMPONENT

RESERVATION DB

GRIDWAY META-SCHEDULER

GLOBUS MIDDLEWARE

GARUDA MIDDLEWARE RESERVATION COMPONENT

GARUDA LRM RESERVATION COMPONENT

RESERVATION REPLICA DB

RESERVATION MANAGER AND SCHEDULER

FAILOVER

LOCAL RESOURCE MANAGER

Garuda Reservation Architecture
garuda reservation features
Garuda Reservation Features
  • Advanced and Immediate Reservation of resources across multiple clusters
    • Ensure resource availability
    • GSI based reservation: Garuda Reservation
    • Grid Reservation Failover mechanism:
    • Application Programming Interface
    • Intelligent resource allocation based on QoS Parameters
    • Virtual Organization support
    • Avoiding resource under utilization
    • Integration with Gridway Meta-scheduler and Globus Middleware
performance metrics
Performance Metrics
  • Mean waiting time
  • Execution time
  • Turnaround time

IEEE HPEC'12

turnaround time
Turnaround Time
  • Turnaround time (total time taken between the submission of a program/process/thread/task (Linux) for execution and the return of the complete output to the customer/user)

Job Submission

Job Output

User

IEEE HPEC'12

conclusion
Conclusion
  • Guarantees the availability of resources
  • Eliminates the waiting time
  • Reduces Turnaround time considerably
  • Well integrates into the Grid Middleware
  • Built for the production infrastructure
  • Analysis has shown results that are really encouraging.

IEEE HPEC'12

thank you
Thank You

IEEE HPEC'12