Multi-objective and multi constrained task scheduling framework for computational grids
Abstract Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task sche...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-56957-8 |
| _version_ | 1797247275230035968 |
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| author | Sujay N. Hegde D. B. Srinivas M. A. Rajan Sita Rani Aman Kataria Hong Min |
| author_facet | Sujay N. Hegde D. B. Srinivas M. A. Rajan Sita Rani Aman Kataria Hong Min |
| author_sort | Sujay N. Hegde |
| collection | DOAJ |
| description | Abstract Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator. |
| first_indexed | 2024-04-24T19:56:06Z |
| format | Article |
| id | doaj.art-8a42d3db7ff34719874c3ddbbdf937b9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T19:56:06Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8a42d3db7ff34719874c3ddbbdf937b92024-03-24T12:20:37ZengNature PortfolioScientific Reports2045-23222024-03-0114113110.1038/s41598-024-56957-8Multi-objective and multi constrained task scheduling framework for computational gridsSujay N. Hegde0D. B. Srinivas1M. A. Rajan2Sita Rani3Aman Kataria4Hong Min5University of California USANitte Meenakshi Institute of TechnologyTCS Research and InnovationGuru Nanak Dev Engineering CollegeAmity Institute of Defence Technology, Amity UniversitySchool of Computing, Gachon UniversityAbstract Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.https://doi.org/10.1038/s41598-024-56957-8Grid computingDirect acyclic graphScientific graphGridSimTOPSIS |
| spellingShingle | Sujay N. Hegde D. B. Srinivas M. A. Rajan Sita Rani Aman Kataria Hong Min Multi-objective and multi constrained task scheduling framework for computational grids Scientific Reports Grid computing Direct acyclic graph Scientific graph GridSim TOPSIS |
| title | Multi-objective and multi constrained task scheduling framework for computational grids |
| title_full | Multi-objective and multi constrained task scheduling framework for computational grids |
| title_fullStr | Multi-objective and multi constrained task scheduling framework for computational grids |
| title_full_unstemmed | Multi-objective and multi constrained task scheduling framework for computational grids |
| title_short | Multi-objective and multi constrained task scheduling framework for computational grids |
| title_sort | multi objective and multi constrained task scheduling framework for computational grids |
| topic | Grid computing Direct acyclic graph Scientific graph GridSim TOPSIS |
| url | https://doi.org/10.1038/s41598-024-56957-8 |
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