Call For Paper Volume:7 Issue:9 Sep'2020 |

Modified fuzzy Logic and Advance Particle Swarm Optimization Model for Cloud Computing

Publication Date : 05/09/2016


DOI : 10.21884/IJMTER.2016.3028.0EHDE

Author(s) :

Umesh Lilhore , Dr Santosh Kumar.


Volume/Issue :
Volume 3
,
Issue 8
(09 - 2016)



Abstract :

In cloud computing better performances of computing resources are always a desirable task for cloud researchers. In this research paper we are presenting a new load balancing algorithm for cloud performance improvement, is introduced which based on “Modified fuzzy Logic and Advance Particle Swarm Optimization Model (MFL-APSOM)”, to optimize the total execution time of tasks in the workflow applications. The key objective of applying the MFL-APSOM method is to minimize the total tasks execution time by verifying the load fluctuations of the interconnected tasks. The variance of the algorithm considers factors such as load variations and optimization of the data retrieval time. The proposed MFL-APSOM model is validated by applying various workflow structures with different data block sizes. The results are compared with existing HEFT (Heterogeneous Earliest Finish Time) algorithm and Scalable Heterogeneous Earliest Finish Time methods (SHEFT).Simulation results clearly shows that proposed method performs outstanding in terms of cloud performance parameters over existing methods.


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Modified fuzzy Logic and Advance Particle Swarm Optimization Model for Cloud Computing

August 27, 2016