The subjective job scheduler with a satisfying criterion based on backpropagation neural network

Title:

The subjective job scheduler with a satisfying criterion based on backpropagation neural network

Creator:

Gopalakrishnan, Anilkumar Kothalil

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:5bee6543-d72a-4005-9968-7e884afe6762
uuid:5bee6543-d72a-4005-9968-7e884afe6762
doi:10.14311/NNW.2012.22.012

Subject:

Greedy task alignment procedure, subjective criteria, satisfying criterion, convergence test, job validity test, and cost evaluation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper aims to present and discuss the concept of a subjective job scheduler with a satisfying criterion based on a Backpropagation Neural Network (BPNN) and a greedy task alignment procedure. The BPNN is to assign priorities to the tasks of each job based on the given subjective criteria. The subjective criteria and the task alignment procedure depend on the solution plan towards a given job scheduling problem depending on the user's need. When the scheduler is provided with a desired job selection criteria and task alignment procedure for the problem, it generates user satisfying solutions for a set of jobs. The satisfying criterion of the scheduler determines the user satisfaction based on three measures: convergence test of the BPNN, validity of the input job set and cost evaluation of the solutions. The simulations and comparisons presented in this paper indicate that the proposed approach is one of the most effective strategies of structuring a subjective functional job scheduler.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:2

Harvested from:

CDK

Metadata only:

false