Hybrid Genetic Swarm Scheduling for Cloud Computing

1
Dr. M.Sridhar
Dr. M.Sridhar
1 R.V.R & J.C COLLEGE OF ENGINEERING, INDIA

Send Message

To: Author

GJCST Volume 15 Issue B3

Article Fingerprint

ReserarchID

CSTB19LE4

Hybrid Genetic Swarm Scheduling for Cloud Computing Banner
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Cloud computing ensures access to shared resources and common infrastructure, offering services on demand over a network for operations to meet changing business needs. Scheduling is a prominent activity that is executed in a cloud computing environment. To increase cloud computing work load efficiency, tasks scheduling is performed to get maximum profit. In cloud, high communication cost prevents task schedulers from being applied in large scale distributed environments. Cloud environment system scheduling is NP-complete. To solve the NP complete and NP hard problems heuristic approaches are used. This study proposes a hybrid optimization based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for scheduling in cloud environments.

24 Cites in Articles

References

  1. Rajveer Kaur,Supriya Kinger (2014). Analysis of Job Scheduling Algorithms in Cloud Computing.
  2. Y Azar,N Ben-Aroya,N Devanur,N Jain (2013). Cloud scheduling with setup cost.
  3. A Vijayalakshmi,Lepakshi,Dr,Prashanth C S R (2013). A Study on Task Scheduling Algorithms in Cloud Computing.
  4. P Salot (2013). A Survey Of Various Scheduling Algorithm In Cloud Computing Environment.
  5. B Santhosh,Dr,D Manjaiah (2014). An Improved Task Scheduling Algorithm based on Max-min for Cloud Computing.
  6. Gaurang Patel,Rutvik Mehta,Upendra Bhoi (2015). Enhanced Load Balanced Min-min Algorithm for Static Meta Task Scheduling in Cloud Computing.
  7. V Kumar (2014). Hybrid optimized list scheduling and trust based resource selection in cloud computing.
  8. Liji Jacob,V Jeyakrishanan,P Sengottuvelan (2014). Resource Scheduling in Cloud using Bacterial Foraging Optimization Algorithm.
  9. S Kaur,A Verma (2012). An efficient approach to genetic algorithm for task scheduling in cloud computing environment.
  10. G Kaur,E Sharma Optimized Utilization of Resources Using Improved Particle Swarm Optimization Based Task Scheduling Algorithms in Cloud Computing.
  11. R Madivi,S Kamath (2014). An hybrid bioinspired task scheduling algorithm in cloud environment.
  12. R Raju,R Babukarthik,D Chandramohan,P Dhavachelvan,T Vengattaraman (2013). Minimizing the makespan using Hybrid algorithm for cloud computing.
  13. K Zhu,H Song,L Liu,J Gao,G Cheng (2011). Hybrid genetic algorithm for cloud computing applications.
  14. A Rasooli,D Down (2012). A hybrid scheduling approach for scalable heterogeneous hadoop systems.
  15. Santiago Iturriaga,Sergio Nesmachnow,Bernabe Dorronsoro,El-Ghazali Talbi,Pascal Bouvry (2013). A Parallel Hybrid Evolutionary Algorithm for the Optimization of Broker Virtual Machines Subletting in Cloud Systems.
  16. Shirin Dehghani Zahedani,Gholamhossin Dastghaibyfard (2014). A hybrid batch job scheduling algorithm for grid environment.
  17. Jawad Ashraf,Thomas Erlebach (2011). A hybrid scheduling technique for grid workflows in advance reservation environments.
  18. Mustafa Alobaedy,Ku Ku-Mahamud (2014). Scheduling jobs in computational grid using hybrid ACS and GA approach.
  19. S Nguyen,M Zhang (2014). A hybrid discrete particle swarm optimisation method for grid computation scheduling.
  20. G Sadasivam,D (2010). A novel parallel hybrid PSO-GA using Map Reduce to schedule jobs in Hadoop data grids.
  21. Saeed Javanmardi,Mohammad Shojafar,Danilo Amendola,Nicola Cordeschi,Hongbo Liu,Ajith Abraham (2014). Hybrid Job Scheduling Algorithm for Cloud Computing Environment.
  22. M Girgis,T Mahmoud,B Abdullatif,A Rabie Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Tabu Search Optimization Methods.
  23. S Aravindh (2012). Hybrid of Ant Colony Optimization and Genetic Algorithm for Shortest Path in Wireless Mesh Networks.
  24. W Liu,L Liu,D Cartes,G (2007). Binary Particle Swarm Optimization Based.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Dr. M.Sridhar. 2015. \u201cHybrid Genetic Swarm Scheduling for Cloud Computing\u201d. Global Journal of Computer Science and Technology - B: Cloud & Distributed GJCST-B Volume 15 (GJCST Volume 15 Issue B3): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Classification
GJCST-B Classification: C.1.3, D.4.1
Version of record

v1.2

Issue date

July 17, 2015

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices
Total Views: 8194
Total Downloads: 2211
2026 Trends
Research Identity (RIN)
Related Research

Published Article

Cloud computing ensures access to shared resources and common infrastructure, offering services on demand over a network for operations to meet changing business needs. Scheduling is a prominent activity that is executed in a cloud computing environment. To increase cloud computing work load efficiency, tasks scheduling is performed to get maximum profit. In cloud, high communication cost prevents task schedulers from being applied in large scale distributed environments. Cloud environment system scheduling is NP-complete. To solve the NP complete and NP hard problems heuristic approaches are used. This study proposes a hybrid optimization based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for scheduling in cloud environments.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]
×

This Page is Under Development

We are currently updating this article page for a better experience.

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Hybrid Genetic Swarm Scheduling for Cloud Computing

Dr. M.Sridhar
Dr. M.Sridhar R.V.R & J.C COLLEGE OF ENGINEERING, INDIA

Research Journals