In the cloud data centers of large technology companies, there are various resources such as computing, communication and storage, which provide stable and quality-guaranteed services for subscribers. To get a compreh...
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ISBN:
(纸本)9798350333077
In the cloud data centers of large technology companies, there are various resources such as computing, communication and storage, which provide stable and quality-guaranteed services for subscribers. To get a comprehensive understanding of real load trends and to observe the characteristics of user tasks consuming resources, we analyzed the workload of Microsoft Azure Virtual Machines (VMs). Specifically, we analyzed the workload of all VMs in the Azure cluster within one month in 2017 and 2019. The existing public data provides complete information for each schema. Through data analysis and fitting, we revealed the consistency and difference in delay and memory usage of workload tasks during the time span from 2017 to 2019. In addition, we found the distribution characteristics of the task computing requirement, i.e., it conforms to a large scale exponential distribution superimposed with a small scale Sample function. We propose two modeling approaches to fit the 2017 and 2019 traces and find that the complex one is able to reduce the Sum of Squares due to Error (SSE) by 72.9% and 66.9% compared to the simple one. The task characteristics we discovered can help researchers understand the workload and provide a modeling basis for the simulation framework.
The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases,which are the most common cause of morbidity and mortality worldwide,have gotten a lot of attention and been widely exp...
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The underlying electrophysiological mechanisms and clinical treatments of cardiovascular diseases,which are the most common cause of morbidity and mortality worldwide,have gotten a lot of attention and been widely explored in recent *** the way,techniques such as medical imaging,computing modeling,and artificial intelligence(AI)have always played significant roles in above *** this article,we illustrated the applications of AI in cardiac electrophysiological research and disease *** summarized general principles of AI and then focused on the roles of AI in cardiac basic and clinical studies incorporating magnetic resonance imaging and computing modeling *** main challenges and perspectives were also analyzed.
This study describes the computational modeling of the atmospheric dispersion of radionuclides resulting from a hypothetical nuclear accident involving an off-site release of the inventory of a Small Modular Reactor (...
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This study describes the computational modeling of the atmospheric dispersion of radionuclides resulting from a hypothetical nuclear accident involving an off-site release of the inventory of a Small Modular Reactor (SMR). The inventory released was simulated with the aid of the SCALE software, considering 2 years of continuous operation. The radionuclide Cs-137 was selected due to its abilities to cause environmental damage. A simulation of the atmospheric dispersion of the radionuclide was performed using the codes HotSpot (analytical) and ANSYS (numerical). The distribution of concentrations and consequent equivalent total effective doses (TEDE) were estimated as a function of the distance from the point of release and the local atmospheric stability conditions. In the study, a hybrid computer simulation refers to the simultaneous application of the analytical and numerical models of the nuclear event. The results from the simulated conditions agree with the environmental Cs-137 concentration values, regardless of whether those values are from a numerical or analytical model. However, there is a disagreement about the location and distribution of these concentrations. It was also verified that the average percentage error of the results obtained in the two simulations was around 5%, considering the evaluation between 400 and 1500 m from the origin of the dispersion. For smaller distances in relation to the release point, the analytical and numerical models produce solutions that, although divergent, were shown to be complementary. The relevance of this investigation shows the importance of the convergence of isolated computing platforms in order to support decision-making.
With the rapid advancements in cloud computing, edge computing, and intelligent devices, there has been a proliferation of computing resources with a trend toward ubiquitous deployment. However, traditional network ar...
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