Improved Hybrid Algorithm Approach based Load Balancing Technique in Cloud Computing

Improved Hybrid Algorithm Approach based Load Balancing Technique in Cloud Computing

Srinivasa Rao Gundu Computer Science Ph.D. Research Scholar & Senior Software Trainer

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T. Anuradha

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α Dravidian University

Improved Hybrid Algorithm Approach based Load Balancing Technique in Cloud Computing

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Abstract

The routine life of modern citizens is completely dominated by the computer aided services. The computer aided services depends on information and communication technologies. The success behind this cloud computing are data centers with virtualization technology equipped with fastest internet and the wide acceptance of the users due to its affordable price to the common people. Effective services can be provided to the end user only when proper scheduling of tasks are done in peak hours when heterogeneous collection of requests are coming to the data center. The load is balanced in such situations, to attain equilibrium position in load. It is a difficult task to balance the load. Load can be balanced using either by software or hardware.

References

16 Cites in Article

Reference Format

Nitin Kumar Mishra,Nishchol Mishra (2015). Load Balancing Techniques: Need, Objectives and Major Challenges in Cloud Computing -A Systematic Review.
Doddini Probhuling,L (2013). Current Scenario in Architect and Applications of Cloud.
Chetna Dabas,Member,J Iaeng,Gupta (2010). A Cloud Computing Architecture Framework for Scalable RFID.
Hong Xu,Baochun Li (2012). A General and Practical Datacenter Selection Framework for Cloud Services.
T Kokilavani,Dr George Amalarethinam (2011). Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing.
Jens Buysse,Konstantinos Georgakilas,Anna Tzanakaki,Marc De Leenheer,Bart Dhoedt,Chris Develder,Piet Demeester (2011). Calculating the Minimum Bounds of Energy Consumption for Cloud Networks.
Wanneng Shu,Wei Wang,Yunji Wang (2014). A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing.
M Hemamalini,M V. (2016). Response Time Minimization Task Scheduling Algorithm.
Ms,Nitika,Ms,Mr Shaveta,Raj Gaurav (2012). Comparative Analysis of Load Balancing Algorithms in Cloud Computing.
Bhuvnesh Pathania,Abhilash Sharma (2017). Improved Hybrid DLBS Artificial Bee Colony Optimization Algorithm based on Parallel Computing Environment.
Jianfeng Zhao,Hongze Qiu (2013). Genetic algorithm and ant colony algorithm based Energy-Efficient Task Scheduling.
Javid Taheri,Albert Zomaya,Howard Siegel,Zahir Tari (2013). Pareto frontier for job execution and data transfer time in hybrid clouds.
Fabrizio Ferrandi,Pier Lanzi,Antonino Tumeo (2013). Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems.
A Nirmala,S Veni (2016). An Enhanced Approach for Performance Improvement using Hybrid Optimization Algorithm with K-means++ in a Virtualized Environment.
Poonam Singh,Maitreyee Dutta,Naveen Aggarwal (2017). Bi-objective HWDO Algorithm for Optimizing Makespan and Reliability of Workflow Scheduling in Cloud Systems.
Saurabh Bilgaiyan,Santwana Sagnika,Madhabananda Das (2014). An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms.

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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.

How to Cite This Article

Srinivasa Rao Gundu. 2019. \u201cImproved Hybrid Algorithm Approach based Load Balancing Technique in Cloud Computing\u201d. Global Journal of Computer Science and Technology - B: Cloud & Distributed GJCST-B Volume 19 (GJCST Volume 19 Issue B2): .

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GJCST Volume 19 Issue B2
Pg. 35- 42

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-B Classification: C.2.4

Submission ReceivedDecember 10, 2018
Peer Review Double Blind
Handling Editor
Accepted January 1, 2019
Published January 15, 2019

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v1.2

Issue date

October 18, 2019

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