1 520 student presentation gridsim – grid modeling and simulation toolkit

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520 Student Presentation

GridSim – Grid Modeling and Simulation Toolkit

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Outline

• Introduction

• GRACE Framework

• Nimrod-G Resource Broker

• GridSim Grid Resource Scheduling Simulation Toolkit

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Introduction

• Inspired from electrical power Grid

• Computational power Grid

• Sharing, aggregation geographically distributed resources

• Low level services(security, information, directory, resource management)

• High level services(resource discovery, cost negotiation, resource selection)

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GRACE Framework

• Grid Architecture for Computational Economy

• builds on the existing Grid systems

• new services for resource management and trading and aggregation

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A generic Grid architecture for computational economy

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Commodity Market Models

• GRP specify their service price

• charge users according to the amount of resource consumed

• GRP submit price specification to GTS

• CPU cycles, storage, software, and network

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Nimrod-G Resource Broker

• economic-based resource management and scheduling algorithms

• user-defined deadline and budget constraints

• schedule optimizations and manages supply and demand

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Architecture of Nimrod-G system

• TFE– creation of jobs – job status

• The scheduler – resource discovery– resource trading– resource selection– job assignment

• Dispatcher – deploys jobs

• Agents – setting up execution environm

ent– transporting the code and data

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The Flow actions in Nimrod-G

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Economic-based Scheduling Algorithms

• Cost Optimization – 1. Sort resources.– 2. assign jobs in turn, without exceeding the deadline.

• Time Optimization – 1. For each resource, calculate the next completion

time for an assigned job – 2. Sort resources by next completion time.– 3. Assign one job to the first resource for which the

cost is within the budget.– 4. Repeat steps 1-3 until done.

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World-Wide Grid (WWG) Testbed

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Parameter Sweep Application

• Create a set of jobs with different parameters• Calculate the angular values from different degrees

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Cost Optimisation Scheduling

Australian peak time Australian off-peak time

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Cost Optimisation Scheduling

Australian peak time Australian off-peak time

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Cost and Time Optimization Scheduling

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Cost and Time Optimization Scheduling

time optimization scheduling cost optimization scheduling

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The need for Simulation Tools

• real testbed is not available

• expensive and time consuming

• limited to a few resources and domains

• testing scheduling algorithms for scalability and adaptability

• scheduler performance is hard to trace

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The core Entities of GridSim

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Event diagram among entities

• internal event • external event

• synchronous event• asynchronous event

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Nimrod-G simulation

1. A set of Gridlets

2. Find resources and their costs, create resource list

3. Select scheduling policy based on user requirement

4. Dispatch Gridlets to resources

5. Submits Gridlets to resources 6. Updates runtime parameter to help

predict job consumption rate

7. Repeat 3-6 till finish all jobs or exceed deadline or budget

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WWG testbed resources simulated using GridSim

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Cost-optimization scheduling

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Cost-optimization scheduling

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Time Optimization Scheduling

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Comparing the Cost and Time Optimization Scheduling

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• 1. Sort resources in increasing order

• 2. assign jobs in turn, without exceeding the deadline

• 3. If more two or more resources have the same cost and capacity, schedule resources based on the Time Optimization algorithm

Cost-Time Optimization Scheduling

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Cost-Time Optimization Scheduling

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Cost vs. Cost-Time optimization scheduling

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Cost vs. Cost-Time optimization scheduling

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Cost vs. Cost-Time optimization scheduling

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Conclusion

• The Cost-Time optimization algorithm is more efficient than Cost optimization algorithm.

• To choose Cost-Time optimization or to choose Time optimization algorithm depends on your deadline and budget.

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Reference1. R. Buyya, D. Abramson, and J. Giddy, A Case for Economy Grid Architecture for

Service-Oriented Grid Computing, Proceedings of the International Parallel and Distributed Processing Symposium:10th IEEE International Heterogeneous Computing Workshop (HCW 2001), April 23, 2001, San Francisco, California, USA, IEEE CS Press, USA, 2001.

2. R. Buyya, D. Abramson, and J. Giddy, Nimrod-G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid, The 4th International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2000), May 2000, Beijing, China, IEEE Computer Society Press, USA.

3. R. Buyya, J. Giddy, D. Abramson, An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications, Proceedings of the 2nd International Workshop on Active Middleware Services (AMS 2000), Kluwer Academic Press, August 1, 2000, Pittsburgh, USA.

4. R. Buyya, M. Murshed, and D. Abramson, A Deadline and Budget Constrained Cost-Time Optimization Algorithm for Scheduling Task Farming Applications on Global Grids, Technical Report, Monash University, March 2002. http://www.buyya.com/gridsim/

5. Rajkumar Buyya and Manzur Murshed, GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing, The Journal of Concurrency and Computation: Practice and Experience (CCPE), Volume 14, Issue 13-15, Wiley Press, Nov.-Dec., 2002.