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Performability Evaluation of Grid Environments Using Stochastic Reward Nets
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Performability Evaluation of Grid Environments Using Stochastic Reward Nets

Category : Grid Computing


Sub Category : DOTNET


Project Code : ITDGC01


Project Abstract

PERFORMABILITY EVALUATION OF GRID ENVIRONMENTS USING STOCHASTIC REWARD NETS

 

 

ABSTRACT

       In this paper, performance of grid computing environment is studied in the presence of failure-repair of the resources. To achieve this, in the first step, each of the grid resource is individually modeled using Stochastic Reward Nets (SRNs), and mean response time of the resource for grid tasks is computed as a performance measure. In individual models, three different scheduling schemes called random selection, non-preemptive priority, and preemptive priority are considered to simultaneously schedule local and grid tasks to the processors of a single resource. In the next step, single resource models are combined to shape an entire grid environment. Since the number of the resources in a large-scale grid environment is more than can be handled using such a monolithic SRN, two approximate SRN models using folding and fixed-point techniques are proposed to evaluate the performance of the whole grid environment. Brouwer’s fixed-point theorem is used to theoretically prove the existence of a solution to the fixed-point approximate model. Numerical results indicate an improvement of several orders of magnitude in the model state space reduction without a significant loss of accuracy.

 

EXISTING SYSTEM

PROPOSED SYSTEM

 EXISTING CONCEPT:-

Processors existing inside a resource can fail

to execute a grid task at any time. When a user existing in a resource’s administrative domain submits a task to the resource, the task is serviced inside that resource along with the grid tasks submitted by grid users.

The performance of a single resource and whole distributed system is highly dependent on the number and processing power of the existing resources and their processors.

PROPOSED CONCEPT:-

In proposed method we model a single grid resource using SRNs and compute mean response time of the resource to grid tasks.

Dependencies among sub-models of the decomposed model are found, and then, fixed-point iteration method is used to solve the interacting sub-models

We proposed approximate performance models for polling systems which can be used to analyze the performance of such systems when the number of nodes and their potential customers are large.

 

EXISTING TECHNIQUE:-

RMS can fail during servicing the grid tasks

PROPOSED TECHNIQUE:-

SRN (Stochastic Reward Nets) Models

TECHNIQUE DEFINITION:-

Processors existing inside a resource can fail to execute a grid task at any time. Therefore, the availability of a grid resource to service grid tasks can be highly influenced by processor failure.

TECHNIQUE DEFINITION:-

SRN is an extension of Stochastic Petri Nets (SPNs) which has the advantage of specifying and evaluating a real system in a compact and intuitive way.

DRAWBACKS:-

Inside a resource can fail.

Doesn’t not reduce network traffic.

ADVANTAGES:-

Performance, availability and reliability analysis of fault tolerant.

Network traffics are reduced.

 

 
 
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