GA–JAYA: A Novel Hybridization Technique to Solving Job Scheduling Problems

2022 ◽  
pp. 221-230
Author(s):  
Biswaranjan Acharya ◽  
Sucheta Panda
Keyword(s):  
Job Scheduling ◽  
Hybridization Technique ◽  
Scheduling Problems

scholarly journals Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ibrahim Attiya ◽  
Mohamed Abd Elaziz ◽  
Shengwu Xiong
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithms ◽  
Scheduling Problems ◽  
It Services ◽  
On Demand

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

scholarly journals Genetic Algorithm for Job Scheduling with Maintenance Consideration in Semiconductor Manufacturing Process

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Seungchul Lee ◽  
Jun Ni
Keyword(s):  
Genetic Algorithm ◽  
Semiconductor Manufacturing ◽  
Job Scheduling ◽  
Optimization Methods ◽  
Optimization Method ◽  
Scheduling Problems ◽  
Simulation Based ◽  
Sequencing Problems ◽  
Maintenance Activities ◽  
Single Cluster

This paper presents wafer sequencing problems considering perceived chamber conditions and maintenance activities in a single cluster tool through the simulation-based optimization method. We develop optimization methods which would lead to the best wafer release policy in the chamber tool to maximize the overall yield of the wafers in semiconductor manufacturing system. Since chamber degradation will jeopardize wafer yields, chamber maintenance is taken into account for the wafer sequence decision-making process. Furthermore, genetic algorithm is modified for solving the scheduling problems in this paper. As results, it has been shown that job scheduling has to be managed based on the chamber degradation condition and maintenance activities to maximize overall wafer yield.

Research on Job Scheduling Problems with DMS for Cell Controller Based Bogie

2013 ◽  
Vol 791-793 ◽  
pp. 595-598
Author(s):  
Xian Hong Wang ◽  
Wen Xu ◽  
Wei Dong Feng
Keyword(s):  
Machine Tools ◽  
Job Scheduling ◽  
Job Shop ◽  
Object Oriented ◽  
Control Model ◽  
Scheduling Problems ◽  
Control Logic ◽  
Cell Control ◽  
Discrete Manufacturing System ◽  
Cell Controller

For tackling job scheduling problems encountered with discrete manufacturing system for 380km/h motor bogie, Object-oriented Petri Net based Job-Shop Cell Controller System (OPNCC) was brought forward identifying categories of all objects within cell control model, describing relationship between categories, analyzing and recognizing cell control logic through the dynamic behavior of physical objects and introducing control decision and strategy into the control logic of as designed OPN model, thereby constituting a complete object-oriented cell control model. With improved genetic algorithm, it can offer dynamic optimization to balanced production of multiple work pieces on several machine tools so that the machine tools can be utilized to a maximum extent.

WBAT Job Scheduler: A Multi-Objective Approach for Job Scheduling Problem on Cloud Computing

2019 ◽  
Vol 29 (06) ◽  
pp. 2050089
Author(s):  
B. Hariharan ◽  
D. Paul Raj
Keyword(s):  
Job Scheduling ◽  
Fitness Function ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Multi Objective ◽  
Job Scheduling Problem ◽  
Job Scheduler ◽  
New Good ◽  
Better Than

The main objective of the proposed methodology is multi-objective job scheduling using hybridization of whale and BAT optimization algorithm (WBAT) which is used to change existing solution and to adopt a new good solution based on the objective function. The scheduling function in the proposed job scheduling strategy first creates a set of jobs and cloud node to generate the population by assigning jobs to cloud node randomly and evaluate the fitness function which minimizes the makespan and maximizes the quality of jobs. Second, the function uses iterations to regenerate populations based on WBAT behavior to produce the best job schedule that gives minimum makespan and good quality of jobs. The experimental results show that the performance of the proposed methods is better than the other methods of job scheduling problems.

scholarly journals Resource Management in Machine Scheduling Problems: A Survey

2007 ◽  
Vol 1 (2) ◽  
pp. 59-89 ◽  
Author(s):  
Adam Janiak ◽  
Władysław Janiak ◽  
Maciej Lichtenstein
Keyword(s):  
Job Scheduling ◽  
Scientific Literature ◽  
Real Life ◽  
Release Dates ◽  
Scheduling Problems ◽  
Release Date ◽  
Processing Times ◽  
Solution Algorithms ◽  
Machine Scheduling Problems ◽  
Scheduling Models

The paper is a survey devoted to job scheduling problems with resource allocation. We present the results available in the scientific literature for commonly used models of job processing times and job release dates, i.e., the models in which the job processing time or the job release date is given as a linear or convex function dependent on the amount of the additional resource allotted to the job. The scheduling models with resource dependent processing times or resource dependent release dates extend the classical scheduling models to reflect more precisely scheduling problems that appear in real life. Thus, in this paper we present the computational complexity results and solution algorithms that have been developed for this kind of problems.

scholarly journals A faster way to approximately schedule equally divided jobs with preemptions on a single machine by subsequent job importance growth

2019 ◽  
Keyword(s):  
Single Machine ◽  
Job Scheduling ◽  
Computation Time ◽  
Relative Advantage ◽  
Release Dates ◽  
Scheduling Problems

The goal of this work is to study whether the input order of the job release dates results in different time of computations in finding an approximate schedule for equally divided jobs with preemptions on a single machine by subsequent job importance growth,. It has been ascertained that the descending job order has a 1 % relative advantage when scheduling more than 200 jobs. With increasing the number of jobs off 1000, the advantage tends to increase. The advantage can grow up to 22%. A maximally possible gain in computation time is obtained in scheduling longer series of bigger-sized job scheduling problems.

Multi-Processor Job Scheduling in High-Performance Computing (HPC) Systems

2020 ◽  
pp. 168-203
Author(s):  
Annu Priya ◽  
Sudip Kumar Sahana
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Combinatorial Problems ◽  
Gpu Programming ◽  
Scheduling Problems ◽  
Processor Scheduling ◽  
Complex Problems ◽  
Gpu Scheduling ◽  
Hard Real Time

Processor scheduling is one of the thrust areas in the field of computer science. The future technologies use a huge amount of processors for execution of their tasks like huge games, programming software, and in the field of quantum computing. In hard real-time, many complex problems are solved by GPU programming. The primary concern of scheduling is to reduce the time complexity and manpower. There are several traditional techniques for processor scheduling. The performance of traditional techniques is reduced when it comes under huge processing of tasks. Most scheduling problems are NP-hard in nature. Many of the complex problems are recently solved by the GPU programming. GPU scheduling is another complex issue as it runs thousands of threads in parallel and needs to be scheduled efficiently. For such large-scale scheduling problem, the performance of state-of-the-art algorithms is very poor. It is observed that evolutionary and genetic-based algorithms exhibit better performance for large-scale combinatorial problems.

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