The main limitation of layer-3 networking is that there is no built-in isolation mechanism comparable to the VLANs in layer-2 networks. Furthermore, the hierarchical nature of IP addresses means that an instance is on the same subnet as its physical host, making migration out of the subnet difficult. For these reasons, network virtualization needs to use IP encapsulation and software at the end hosts. This is for isolation and the separation of the addressing in the virtual layer from the addressing in the physical layer. Other potential disadvantages of layer-3 networking include the need to design an IP addressing scheme rather than relying on the switches to keep track of the MAC addresses automatically, and to configure the interior gateway routing protocol in the switches.
时间: 2024-04-22 15:26:40 浏览: 28
第三层网络的主要限制是没有内建的与第二层网络中的VLAN相媲美的隔离机制。此外,IP地址的分层结构意味着实例和其物理主机位于同一个子网中,使得迁移到子网之外变得困难。因此,网络虚拟化需要在终端主机上使用IP封装和软件来实现隔离,并将虚拟层中的寻址与物理层中的寻址分离。第三层网络的其他潜在缺点包括需要设计一个IP寻址方案,而不是依靠交换机自动跟踪MAC地址,并需要在交换机上配置内部网关路由协议。
相关问题
Cef.Shutdown has already been called. Cef.Initialize can only be called once per process. This is a limitation of the underlying CEF/Chromium framework. Calling Cef.Initialize after Cef.Shutdown is not supported. You can change many (not all) settings at runtime through RequestContext.SetPreference.See https://github.com/cefsharp/CefSharp/wiki/General-Usage#request-context-browser-isolation”
嗨,根据你提供的错误信息,似乎你在调用Cef.Initialize之前已经调用了Cef.Shutdown。这是因为CEF/Chromium框架的限制,每个进程只能调用一次Cef.Initialize。在调用Cef.Shutdown后再次调用Cef.Initialize是不被支持的。
如果你需要在程序运行期间更改一些设置,你可以通过RequestContext.SetPreference方法来改变(并非所有)设置。你可以参考链接https://github.com/cefsharp/CefSharp/wiki/General-Usage#request-context-browser-isolation,了解更多关于如何使用RequestContext进行浏览器隔离以及设置的信息。
如果你想要重新初始化CEF,你需要确保在调用Cef.Shutdown之后不再调用Cef.Initialize,并且在程序的生命周期内只调用一次Cef.Initialize。
希望这可以帮助解决你的问题。如果还有其他疑问,请随时提问。
Combining 3D Morphable Models: A Large scale Face-and-Head Model
Combining 3D Morphable Models: A Large scale Face-and-Head Model is a research paper that proposes a method to combine existing 3D Morphable Models (3DMMs) of face and head shapes to create a larger and more diverse model. The authors argue that existing 3DMMs suffer from limited variation due to the small number of samples used to create them, and that combining multiple models can this limitation.
The proposed method involves aligning the different 3DMMs using a common reference frame, and then merging them into a single model. To ensure that the resulting model is still statistically representative of the population, the authors use a weighting scheme that takes into account the relative size of each dataset and the variance within each dataset.
The final model is evaluated on several tasks, including face recognition and 3D face reconstruction from a single image. The results show that the combined model outperforms existing models in terms of accuracy and diversity.
Overall, the paper presents a promising approach to creating more comprehensive 3DMMs that can better capture the variability of human faces and heads.
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