Main Reference PaperPredicting Host CPU Utilization in the Cloud using Evolutionary Neural Networks, Future Generation Computer Systems, 2018, [Java/CloudSim].
  • The aim of this project is to investigate if neural networks are capable of accurately predicting CPU utilization for a short time. The three algorithms (PSO, DE, and CMA-ES) can train a network to accurately predict CPU utilization within thousands of evaluations of the training data. These algorithms evaluate CMA-ES performs the best on the training data followed by DE and lastly PSO. On the test data, however, CMA-ES and DE perform equally. It is realized that the prediction of the CPU utilization is a difficult task due to the occasional sudden extreme change in CPU utilization. All prediction algorithms struggle to predict these rapid changes. It is also shown that the evolved networks are also capable of accurately predicting are given new CPU utilization data from both the same and new hosts. It indicates that there are some reoccurring patterns and regularities in the CPU utilization data that the networks are capable of exploiting to give a good general performance. It is also found that the networks prediction accuracy does decrease as it predicts further a reasonable level of accuracy.

+ Description
  • The aim of this project is to investigate if neural networks are capable of accurately predicting CPU utilization for a short time. The three algorithms (PSO, DE, and CMA-ES) can train a network to accurately predict CPU utilization within thousands of evaluations of the training data. These algorithms evaluate CMA-ES performs the best on the training data followed by DE and lastly PSO. On the test data, however, CMA-ES and DE perform equally. It is realized that the prediction of the CPU utilization is a difficult task due to the occasional sudden extreme change in CPU utilization. All prediction algorithms struggle to predict these rapid changes. It is also shown that the evolved networks are also capable of accurately predicting are given new CPU utilization data from both the same and new hosts. It indicates that there are some reoccurring patterns and regularities in the CPU utilization data that the networks are capable of exploiting to give a good general performance. It is also found that the networks prediction accuracy does decrease as it predicts further a reasonable level of accuracy.

  • To predict CPU utilization with a high degree of accuracy for short periods and on data sets that have sudden extreme changes.

+ Aim & Objectives
  • To predict CPU utilization with a high degree of accuracy for short periods and on data sets that have sudden extreme changes.

  • It can be extended to predict the amount of RAM and disk utilization in hosts.

+ Contribution
  • It can be extended to predict the amount of RAM and disk utilization in hosts.

  • Java Development Kit 1.8.0, MySQL 5.5.40. Cloudsim-4.0 with WorkflowSim-1.0.

  • Netbeans 8.0.1, J2SE.

+ Software Tools & Technologies
  • Java Development Kit 1.8.0, MySQL 5.5.40. Cloudsim-4.0 with WorkflowSim-1.0.

  • Netbeans 8.0.1, J2SE.

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

+ Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

+ Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

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