An Enhanced Model for Job Sequencing and Dispatch in Identical Parallel Machines

U I Onwuachu ,C Ugwu ,Edem Williams

doi:10.7176/ceis/11-2-05

Abstract

This paper has developed an efficient scheduling model that is robust and minimizes the total completion time for job completion in identical parallel machines. The new model employs Genetic-Fuzzy technique for job sequencing and dispatch in identical parallel machines. It uses genetic algorithm technique to develop a job scheduler that does the job sequencing and optimization while fuzzy logic technique was used to develop a job dispatcher that dispatches job to the identical parallel machines. The methodology used for the design is the Object Oriented Analysis and Design Methodology (OOADM) and the system was implemented using C# and .NET framework. The model was tested with fifteen identical parallel machines used for printing. The parameters used in analyzing this model include the job scheduling length, average execution time, load balancing and machines utilization. The result generated from the developed model was compare with the result of other job scheduling models like First Come First Sever (FCFS) scheduling approach and Genetic Model (GA) scheduling approach. The result of the new model shows a better load balancing and high machine utilization among the individual machines when compared with the First Come First Serve (FCFS) scheduling model and Genetic Algorithm (GA) scheduling model. Keywords : Parallel Machines, Genetic Model, Job Scheduler, Fuzzy Logic Technique, Load Balancing, Machines Utilization DOI: 10.7176/CEIS/11-2-05 Publication date: March 31 st 2020

Highlights

Scheduling is an important issue in any type of system (Ramamritham and Stankovic,1994)
This paper identifies a number of performance issues that may be experienced when utilizing the identical parallel machine in the scenario of job scheduling, namely mapping, arrangement of execution, and optimal configuration of the identical parallel machines
The result shows that there is better load balancing among the individual machines when compared with the first come first serve (FCFS) scheduling approach and Genetic Algorithm (GA) scheduling approach

Summary

Introduction

Scheduling is an important issue in any type of system (Ramamritham and Stankovic,1994). The challenges of job scheduling in a parallel machine are dealing with limited computing resources for the number of jobs, considering the following factors which include, resource starvation, load balancing complexity, dependency and efficiency (Rachhpal, 2016). Utilizing optimization and dispatching rules are two well-known techniques for taking care of scheduling issues. The way jobs are allocated to machine is fundamental to achieving high performance within the parallel systems, these includes minimizing the job response time and maximizing system throughput (Neelu and Sampada, 2012). This paper identifies a number of performance issues that may be experienced when utilizing the identical parallel machine in the scenario of job scheduling, namely mapping, arrangement of execution, and optimal configuration of the identical parallel machines. The enabling strategy is the use of job scheduling plan based on Genetic Algorithm and fuzzy logic to determine scheduling issues (Ramkumar et al, 2011)

Literature Review

Materials and Methods

Discussion

Conclusion