Proposal for an optimal job allocation method for data-intensive applications based on multiple costs balancing in a hybrid cloud environment
Due to the explosive increase in the amount of information in computer systems, we need a system that can process large amounts of data efficiently. Cloud computing system is an effective means to achieve this capacity and has spread throughout the world. In our research, we focus on hybrid cloud environments, and we propose a method for efficiently processing large amounts of data while responding flexibly to needs related to performance and costs. We have developed this method as middleware.
 For data-intensive jobs using this system, we have created a benchmark that can determine the saturation of the system resources deterministically. Using this benchmark, we can determine the parameters in this middleware. This middleware can provide Pareto optimal cost load balancing based on the needs of the user. The results of the evaluation indicate the success of the system.
 We then compare the processing time when these jobs are processed sequentially and the processing time using this measurement.