NDLI: Dynamic scheduling of computer tasks using genetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Gonzalez Pico, C.A. Wainwright, R.L.
Copyright Year	1994
Description	Author affiliation: Dept. of Math. & Comput. Sci., Tulsa Univ., OK, USA (Gonzalez Pico, C.A.; Wainwright, R.L.)
Abstract	We concentrate on non-preemptive hard real-time scheduling algorithms. We compare FIFO, EDLF, SRTF and genetic algorithms for solving this problem. The objective of the scheduling algorithm is to dynamically schedule as many tasks as possible such that each task meets its execution deadline, while minimizing the total delay time of all of the tasks. We present a MicroGA that uses a small population size of 10 chromosomes, running for 10 trials using a rather high mutation rate with a sliding window of 10 tasks. The steady-state GA was determined to be better than the generational GA for our MicroGA. We also present a parallel MicroGA model designed for parallel processors. The parallel MicroGA works best when migration is used to move tasks from one processor to another to even out the load as much a possible. Test cases show that the sequential MicroGA model and the parallel MicroGA model produced superior task schedules compared to other algorithms tested.<>
Starting Page	829
Ending Page	833
File Size	491935
Page Count	5
File Format	PDF
ISBN	0780318994
DOI	10.1109/ICEC.1994.349947
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dynamic scheduling Genetic algorithms Scheduling algorithm Processor scheduling Sequential analysis Delay effects Biological cells Genetic mutations Steady-state Process design
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Adaptive Steady State Genetic Algorithm for scheduling university exams

Dynamic Scheduling of Computer Tasks Using Genetic Algorithms (1994)

Comparison of steady state and elitist selection genetic algorithms

A Self-Adaptive Genetic Algorithm for Tasks Scheduling in Multiprocessor System

Dynamic real-time scheduling for multi-processor tasks using genetic algorithm

Dynamic scheduling of real-time tasks, by assignment

Task Scheduling in Multiprocessor System Using Genetic Algorithm

Comparison of Batch Scheduling for Identical Multi-Tasks Jobs on Heterogeneous Platforms

Scheduling periodic distributed hard real-time tasks

Dynamic scheduling of computer tasks using genetic algorithms

Similar Documents

Adaptive Steady State Genetic Algorithm for scheduling university exams

Dynamic Scheduling of Computer Tasks Using Genetic Algorithms (1994)

Comparison of steady state and elitist selection genetic algorithms

A Self-Adaptive Genetic Algorithm for Tasks Scheduling in Multiprocessor System

Dynamic real-time scheduling for multi-processor tasks using genetic algorithm

Dynamic scheduling of real-time tasks, by assignment

Task Scheduling in Multiprocessor System Using Genetic Algorithm

Comparison of Batch Scheduling for Identical Multi-Tasks Jobs on Heterogeneous Platforms

Scheduling periodic distributed hard real-time tasks

Dynamic scheduling of computer tasks using genetic algorithms