A Principal Component Analysis and Clustering based Load Balancing Strategy for Cloud Computing
A Principal Component Analysis and Clustering based Load Balancing Strategy for Cloud Computing

Summary/Abstract: The objective of the research is to develop a model based on Principal Component Analysis and clustering for batch-processing load balancing in the cloud computing environment. The findings show that the model is able to extract the current computing resources of the physical hosts and cluster the hosts based on their similarity features. The computing resources of the virtual machine for a new requested task is then extracted and matched with the hosts clusters to select the most suitable physical hosts based on the computing resources. Conversely, when compared with the simulation results of the Round Robin and First Come First Serve load balancing models, the proposed method shows a decrease failure rate of task deployment events.

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