In response to the COVID-19 pandemic, a lot of scholarly articles have been published recently and made freely available. At the same time, there is emerging need to provide reliable and adequate information, which can reinforce the efforts made in raising awareness, to carry out infallible actions to prevent the worsening of the pandemic situation. With that said the current work tackles the perennial problem of creating a deep learning system allowing answering with a high precision to questions on a determined subject. To do this, we will use open source scientific and academic articles concerning Covid-19, then we will proceed to the selection of suitable documents which will allow us to feed a question answering system based on BERT fine-tuned on the SQuAD benchmark.
Cloud users can have access to the service based on “pay as you go.” The daily increase of cloud users may decrease the performance, the availability and the profitability of the material and software resources used in cloud service. These challenges were solved by several load balancing algorithms between the virtual machines of the data centers. In order to determine a new load balancing improvement; this article's discussions will be divided into two research axes. The first, the pre-classification of tasks depending on whether their characteristics are accomplished or not (Notion of Levels). This new technique relies on the modeling of tasks classification based on an ascending order using techniques that calculate the worst-case execution time (WCET). The second, the authors choose distributed datacenters between quasi-similar virtual machines and the modeling of relationship between virtual machines using the pre-scheduling levels is included in the data center in terms of standard mathematical functions that controls this relationship. The key point of the improvement, is considering the current load of the virtual machine of a data center and the pre-estimation of the execution time of a task before any allocation. This contribution allows cloud service providers to improve the performance, availability and maximize the use of virtual machines workload in their data centers.
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