An Intelligent Cloud Workflow Scheduling System With Time Estimation and Adaptive Ant Colony Optimization

2019 ◽  
pp. 1-16 ◽  
Author(s):  
Ya-Hui Jia ◽  
Wei-Neng Chen ◽  
Huaqiang Yuan ◽  
Tianlong Gu ◽  
Huaxiang Zhang ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Time Estimation ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Scheduling System

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

scholarly journals Jswa An Improved Algorithm For Grid Workflow Scheduling Using Ant Colony Optimization

2013 ◽  
Vol 06 (04) ◽  
pp. 315-331 ◽  
Author(s):  
Emetis Niazmand ◽  
Javad Bayrampoor ◽  
Arash Ghorbannia Delavar ◽  
Ali Reza Khalili Boroujeni
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Grid Workflow ◽  
Improved Algorithm

Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

2015 ◽  
Vol 14 (10) ◽  
pp. 6176-6183
Author(s):  
S.J. Mohana ◽  
Dr.M. Saroja ◽  
Dr.M. Venkatachalam
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Ant Colony Optimization Algorithm ◽  
Computing Environment ◽  
Shared Resources ◽  
Cloud Computing Environment ◽  
Computing Model

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

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