In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.

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Recently, the cloud service has the potential to replace conventional cluster and grid systems. The objective of migrating apps to the cloud is to minimize maintenance and procurement expenses while simultaneously boosting scalability and availability. However, embra=cing cloud technology created some challenges, such as the complexity of cloud storage. In addition, many clients underestimate if it is not plug-and-play. Each vendor has its access methods, and nonstandard application programming interfaces (APIs) make integrated applications, such as archiving or sharing data with cloud storage, complicated, costly, and require high throughput. Furthermore, organizations did not have many alternatives for implementing high-performance object storage systems in the cloud and on-premises data centers until now. In this paper, we would like to suggest a storage gateway as a solution to this issue and will optimize it using Transfer Acceleration and Diff algorithms to improve the performance, Intelligent Tiering to reduce costs, and Server-Side encryption for extra protection. Moreover, utilizing Storage Gateway has proven can provide more efficient integration between the on-premises data center environment and the AWS Cloud Storage ecosystem that is safer and more reliable. This technology can work in a common data center environment regardless of the vendor used by the company it can communicate seamlessly with the AWS Environment.