[Cloud Computing]Mechanisms: Virtual Network

本文介绍了虚拟网络的概念及其组成,包括虚拟交换机与物理网络之间的连接方式。通过实例展示了如何使用多个虚拟交换机来隔离不同虚拟机间的网络流量,并强调了虚拟网络在管理虚拟服务器与物理网络间连通性及流量方面的作用。

Virtual Network

The virtual network is a combination of virtual switches and their uplinks to a physical network that isolates a network environment. It requires a minimum of one physical uplink and one virtual switch, although it can have more virtual switches.

Virtual networks are used to manage the connectivity and traffic between virtual servers and the physical network. Some of the features and limitations of virtual networks and virtual switches are determined by the hypervisor.

As shown in Figure 1, a host has six physical NICs connected to the corporate physical switch. Virtual Switch A was created with 2 NICs, Virtual Switch B with no NICs, and Virtual Switch C with 2 NICs. Network policies have forced some of the VMs to connect to the 3 virtual switches independently. The virtual network consists of Virtual Switch A, Virtual Switch B, Virtual Switch C, and 6 uplinks.

Figure 1 - An example of a virtual network.

Related Patterns:

基于STM32 F4的永磁同步电机无位置传感器控制策略研究内容概要:本文围绕基于STM32 F4的永磁同步电机(PMSM)无位置传感器控制策略展开研究,重点探讨在不依赖物理位置传感器的情况下,如何通过算法实现对电机转子位置和速度的精确估计与控制。文中结合嵌入式开发平台STM32 F4,采用如滑模观测器、扩展卡尔曼滤波或高频注入法等先进观测技术,实现对电机反电动势或磁链的估算,进而完成无传感器矢量控制(FOC)。同时,研究涵盖系统建模、控制算法设计、仿真验证(可能使用Simulink)以及在STM32硬件平台上的代码实现与调试,旨在提高电机控制系统的可靠性、降低成本并增强环境适应性。; 适合人群:具备一定电力电子、自动控制理论基础和嵌入式开发经验的电气工程、自动化及相关专业的研究生、科研人员及从事电机驱动开发的工程师。; 使用场景及目标:①掌握永磁同步电机无位置传感器控制的核心原理与实现方法;②学习如何在STM32平台上进行电机控制算法的移植与优化;③为开发高性能、低成本的电机驱动系统提供技术参考与实践指导。; 阅读建议:建议读者结合文中提到的控制理论、仿真模型与实际代码实现进行系统学习,有条件者应在实验平台上进行验证,重点关注观测器设计、参数整定及系统稳定性分析等关键环节。
### Serverless Architecture Overview Serverless architecture is a cloud computing model where the cloud provider dynamically manages the allocation and provisioning of servers. Despite the name, it does not mean that servers are absent, but rather that developers do not need to be concerned with the underlying infrastructure. The primary focus in a serverless architecture is on writing and deploying code in the form of functions[^1]. Functions in serverless architectures are stateless and event-driven, allowing them to scale automatically in response to demand. This model optimizes for scalability and cost efficiency, as resources are consumed only when the function is executing. The deployment granularity in serverless is a function, which allows for rapid, independent scaling based on input rates[^1]. #### Key Components of Serverless Architecture 1. **Function as a Service (FaaS)**: FaaS is the core component of serverless computing, where individual functions are deployed and executed in response to events. 2. **Backend as a Service (BaaS)**: BaaS provides developers with pre-built backend services such as databases, authentication, and storage, which can be integrated into the application via APIs. 3. **Event Sources**: These are the triggers that invoke functions, such as HTTP requests, database changes, or messages from a queue. 4. **Orchestration Tools**: Tools like AWS Step Functions or Azure Logic Apps help manage the flow of data between functions and services. #### Security Considerations Security in serverless architectures is inherently different from traditional server-based models. By default, services are isolated within their own service networks, including Virtual Private Clouds (VPCs), which restrict access to only necessary services. This isolation provides a strong foundation for security without requiring additional configuration like security groups or network ACLs. Application-level authentication and authorization further enhance security by ensuring that only authorized entities can invoke functions or access data[^2]. ### Best Practices for Serverless Architecture 1. **Design for Statelessness**: Since functions are ephemeral, they should not rely on local state. Any state should be stored externally, such as in a database or object storage service. 2. **Optimize Function Granularity**: Functions should be small and focused on a single task to maximize scalability and maintainability. Overly large functions can lead to inefficiencies and increased costs. 3. **Implement Proper Error Handling**: Functions should include robust error handling to deal with failures gracefully. Retry mechanisms and dead-letter queues can help manage failed invocations. 4. **Monitor and Log Effectively**: Use cloud provider tools to monitor function execution, track performance metrics, and log errors. This helps in debugging issues and optimizing performance. 5. **Secure Your Functions**: Implement authentication and authorization at the function level using API keys, OAuth tokens, or other mechanisms. Limit the permissions of the function to only what is necessary (principle of least privilege). 6. **Use CI/CD Pipelines**: Automate the deployment process using continuous integration and continuous delivery pipelines to ensure consistent and reliable deployments. 7. **Manage Dependencies Carefully**: Keep dependencies minimal and up-to-date to reduce the attack surface and improve performance. Use container images or layers to manage dependencies efficiently. 8. **Plan for Cold Starts**: Cold starts can introduce latency in function execution. To mitigate this, consider keeping functions warm by using scheduled events or pre-warming techniques. ### Example: Deploying a Simple Serverless Function Below is an example of a simple serverless function written in Python using AWS Lambda: ```python import json def lambda_handler(event, context): # Parse the input event input_data = event.get('input', '') # Process the input result = f"Processed: {input_data}" # Return the response return { 'statusCode': 200, 'body': json.dumps({'result': result}) } ``` This function takes an input string, processes it, and returns a response. It can be triggered by various event sources, such as an API Gateway, S3 bucket, or DynamoDB stream. ###
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