“Hybrid Cloud Storage” (HCS) is a widely adopted framework that combines the functionality of public and private cloud storage models to provide storage services. This kind of storage is especially ideal for organizations that seek to reduce the cost of their storage infrastructure with the use of “Public Cloud Storage” as a backend to on-premises primary storage. Despite the higher performance, the hybrid cloud has latency issues, related to the distance and bandwidth of the public storage, which may cause a significant drop in the performance of the storage systems during data transfer. This issue can become a major problem when one or more private storage nodes fail. In this paper, we propose a new framework for optimizing the data uploading process that is currently used with hybrid cloud storage systems. The optimization is concerned with spreading the data over the multiple storages in the HCS system according to some predefined objective functions. Furthermore, we also used Network Coding technics for minimizing data transfer latency between the receiver (private storages) and transmitter nodes.
In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model's primary goal is to identify potential malfunctions at an early stage to ensure the production process' continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.
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