Today, cloud environments are widely used as execution platforms for most applications. In these environments, virtualized applications often share computing resources. Although this increases hardware utilization, resources competition can cause performance degradation, and knowing which applications can run on the same host without causing too much interference is key to a better scheduling and performance. Therefore, it is important to predict the resource consumption profile of applications in their subsequent iterations. This work evaluates the use of machine learning techniques to predict the increase or decrease in computational resources consumption. The prediction models are evaluated through experiments using real and benchmark applications. Finally, we conclude that some models offer significantly better performance when compared to the current trend of resource usage. These models averaged up to 94% on the F1 metric for this task.

The image-based cargo inspection systems are generally intended to identify illegal practices. However, imaging processes from the scanning of motorized containers have been used for increasing industrial safety. This is made possible by the application of high energy particle linear accelerators (linac). Measurements of both the environmental equivalent dose rate (δH*(10), and the integrated environmental equivalent dose (H*(10) were performed. The estimation of δH*(10) and H*(10) in the scanning channel, including the driver's cab of the truck transporting the container, is critical. The aim of this study is to check the radiological safety for occasional users (drivers) by comparing the levels of δH*(10) and H*(10) with those adopted for public exposure. The study was experimentally conducted in a cargo and container inspection facility that uses a linac operating at the maximum energy of 4.5 MeV. During the in-situ measurements a SpiR-ID identifier detector Model F 8929 MGP manufactured by Mirion Technologies was used. The measurements in the driver's cab suggest that the procedure can be considered safe. However, a fast transient of δH*(10) was identified recording raised values (peaks) around 210x the threshold for public exposures in the first 9 s of each scanning procedure.

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