Main Reference PaperPredicting cloud performance for HPC applications before deployment, Future Generation Computer Systems, 2018 [Java/CloudSim].
  • This project proposes a machine-learning methodology to support the user in the selection of the best cloud configuration to run the target workload before deploying it in the cloud. It enables the user to decide if and what to buy before facing the cost of porting and analyzing the application in the cloud. It couples a cloud-performance-prediction model (CP) on the cloud-provider side with a hardware-independent profile-prediction model (PP) on the user-side. PP captures the application-specific scaling behavior. The user profiles the target application while processing small datasets on small machines, and applies machine learning to generate PP to predict the profiles for larger datasets to be processed in the cloud. CP is generated by the cloud provider to learn the relationships between the hardware-independent profile and cloud performance starting from the observations gathered by executing a set of training applications on a set of training cloud configurations. Since the profile data in use is hardware-independent, the user and the provider can generate the prediction models independently possibly on heterogeneous machines.

Description
  • This project proposes a machine-learning methodology to support the user in the selection of the best cloud configuration to run the target workload before deploying it in the cloud. It enables the user to decide if and what to buy before facing the cost of porting and analyzing the application in the cloud. It couples a cloud-performance-prediction model (CP) on the cloud-provider side with a hardware-independent profile-prediction model (PP) on the user-side. PP captures the application-specific scaling behavior. The user profiles the target application while processing small datasets on small machines, and applies machine learning to generate PP to predict the profiles for larger datasets to be processed in the cloud. CP is generated by the cloud provider to learn the relationships between the hardware-independent profile and cloud performance starting from the observations gathered by executing a set of training applications on a set of training cloud configurations. Since the profile data in use is hardware-independent, the user and the provider can generate the prediction models independently possibly on heterogeneous machines.

  • Maximizing performance and minimizing the execution cost of the prediction models is at most.

Aim & Objectives
  • Maximizing performance and minimizing the execution cost of the prediction models is at most.

  • Prediction model is used to maximize the performance

Contribution
  • Prediction model is used to maximize the performance

  • 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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