Genetic Clustering with Workload Multi-task Scheduler in Cloud Environment


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


One of the challenging problems in the cloud environment is many-task computing paradigm as they contain large volumes of datasets and loosely coupled tasks. The data possession scheme using the cooperative provable model (CPDP) was based on homomorphism that provided reliability by automatically maintaining the multiple photocopy of information. In specific, the CPDP scheme for huge files still required to address the cluster network model for dynamically updating the CPDP parameters. Another scheduling scheme based on the multi-objective (MOS) scheme was specifically designed and applied using the ordinal optimization (OO) method for clouds. In addition, MOS also used different memory and disk requirements, increasing the workload while performing multi-tasking. To increase the performance during multi-tasking, Genetic Clustering with Workload Multi-task (GCWM) scheduler scheme is introduced. GCWM scheduler is based on clustering of similar workload using the genetic concepts which minimizes the computational cost and complexity involved during computation. GCWM scheduler scheme is applied to cluster ‘n’ tasks with initial population (i.e.,) tasks, selection, crossover and mutation operators for workload management. The fitness function in GCWM scheduler scheme cluster similar task in cloud zone and communicate with each other effectively. Genetic Clustering with Workload Multi-task scheduling scheme uses distributed computing resources. GCWM Scheduler ensures the multi tasking operation with efficient users' communication. Genetic Clustering Based Workload Multi-task scheduling scheme is experimented on the factors such as throughput, workload management efficacy, relative cost, and multi-task cluster effect.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Multi-task, Genetic Clustering, Users' communication, Fitness Function, Selection, Mutation operator, Cloud Services, Workload Management

Full Text:

PDF


References


Yan Zhu., Hongxin Hu., Gail-Joon Ahn., Mengyang Yu., “Cooperative Provable Data Possession for Integrity Verification in Multi-Cloud Storage,” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS., 2012.

Fan Zhanga., Junwei Caob., Keqin Li., Samee U. Khand., Kai Hwang., “Multi-objective scheduling of many tasks in cloud platforms,” Future Generation Computer Systems., Elsevier journal., 2013.

Imad M. Abbadi., and Anbang Ruan., “Towards Trustworthy Resource Scheduling in Clouds,” IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 6, JUNE 2013.

Suresh Kumar V.S, Chandra Sekar. A,” Fuzzy-GA Optimized Multi-Cloud Multi-Task Scheduler For Cloud Storage And Service Applications”, International Journal of Scientific & Engineering Research Volume 4, Issue3, March 2013

Fan Zhang, Junwei Cao, Wei Tan, Samee U. Khan, Keqin Li, and Albert Y. Zomaya,,” Evolutionary Scheduling of Dynamic Multitasking Workloads for Big-data Analytics in Elastic Cloud”, IEEE Transactions on Cloud, Oct 2013

Konstantinos Tsakalozos., Mema Roussopoulos., and Alex Delis., “Hint-Based Execution of Workloadsin Clouds with Nefeli,” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 7, JULY 2013.

Hameed Hussain, Saif Ur Rehman Malik, Abdul Hameed, Samee Ullah Khan , Gage Bickler , Nasro Min-Allah , Muhammad Bilal Qureshi , Limin Zhang , Wang Yongji Nasir Ghani , Joanna Kolodziej , Albert Y. Zomaya , Cheng-Zhong Xu , Pavan Balaji Abhinav Vishnu , Fredric Pinel , Johnatan E. Pecero , Dzmitry Kliazovich , Pascal Bouvry , Hongxiang Li , Lizhe Wang, Dan Chen, Ammar Rayes,” A survey on resource allocation in high performance distributed computing systems”, Parallel Computing, Elsevier, Oct 2013

Xiangping Bu., Jia Rao., Cheng-Zhong Xu., “Coordinated Self-configuration of Virtual Machines and Appliances using A Model-free Learning Approach,” IEEE Transactions on Parallel and Distributed Systems, (Volume:24 , Issue: 4 ), 2013.

Abhishek Verma., Ludmila Cherkasova., and Roy H. Campbell., “Orchestrating an Ensemble of MapReduce Jobs for Minimizing Their Makespan,” IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, APRIL 2013.

Yong Cui., Hongyi Wang., Xiuzhen Cheng., Dan Li., and Antti Yla-Jaaski., “Dynamic Scheduling for Wireless Data Center Networks,” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, May 2013

Zhen Cheng, Dechen Zhan, Xibin Zhao, and Hai Wan,” Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing”, Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2014

Haiyan Guan., Jonathan Li., Liang Zhong., Yongtao Yub., Michael Chapman., “Process virtualization of large-scale lidar data in a cloud computing environment,” A Computers & Geosciences., Elsevier journal., 2013.

V. Venkatesa Kumar and K. Dinesh., “Job Scheduling Using Fuzzy Neural Network Algorithm in Cloud Environment,” Bonfring International Journal of Man Machine Interface, Vol. 2, No. 1, March 2012.

Pooyan Jamshidi., Aakash Ahmad., and Claus Pahl., “Cloud Migration Research: A Systematic Review,” IEEE TRANSACTIONS ON CLOUD COMPUTING., 2013.

Hamzeh Khazaei., Jelena Misic, and Vojislav B. Misic., “Performance of Cloud Centers with High Degree of Virtualization under Batch Task Arrivals,” IEEE TRANSACTION ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. X, NO. Y, 2012.

Amir Vahid Dastjerdi., and Rajkumar Buyya., “Compatibility-aware Cloud Service Composition Under Fuzzy Preferences,” IEEE TRANSACTIONS ON CLOUD COMPUTING, 2012.

M.Vijayalakshmi.,Venkatesa Kumar., “Investigations on Job Scheduling Algorithms in Cloud Computing,” International Journal of Advanced Research in Computer Science & Technology, Vol. 2 Issue Special 1 Jan-March 2014.

Jianhua Tang., Wee Peng Tay., and Yonggang Wen., “Dynamic Request Redirection and Elastic Service Scaling in Cloud-Centric Media Networks.” IEEE Transactions on Multimedia., 2013.

Ms. Shubhangi D. Patil1., Dr. S. C. Mehrotra., “Resource Allocation and Scheduling in the Cloud,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 1, Issue 1, May-June 2012.

Vignesh V., Sendhil Kumar KS., Jaisankar N., “Resource Management and Scheduling in Cloud Environment,” International Journal of Scientific and Research Publications, Volume 3, Issue 6, June 2013.

En-Hao Chang., Chen-Chieh Wang., Chien-Te Liu., Kuan-Chung Chen., and Chung-Ho Chen, “Virtualization Technology for TCP/IP Offload Engine,” JOURNAL OF LATEX CLASS FILES, VOL. 11, NO. 4, DECEMBER 2013.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize