开始 通过我们的介绍性内容提高自己的速度。

How to develop edge computing solutions

这是四部分系列的第二部分。从第一篇文章

在考虑如何使用Edge Computing Solutions架设尺度时,重要的是要在系统级环境中谈论硬件和软件。根据经验,这两个方面的需求越来越接近设备边缘。

I created the chart below to help visualize this dynamic. The blue line represents hardware complexity and the green line indicates software complexity. The X-axis represents the continuum from the cloud down through various edge categories, ultimately ending at the device edge in the physical world.

Figure 1: Inherent level of hardware and software customization from cloud to device edge. Source: Jason Shepherd of Dell Technologies

Hardware gets custom faster than software as you approach the device edge

在这个连续体中,有一些关键趋势会影响体系结构和设计决策IoT and edge computing。从硬件镜头中,当您进入远程磁场边缘时,您需要考虑升高的热支持以在密封的网络柜中24/7进行运行,以及潜在的特定于电信的设备认证。

遵循传统数据中心左侧左侧的蓝线,请注意硬件复杂性的增长速度比软件更快。当您接近IoT和Edge Gateway级计算时,您开始看到非常具体的I/O和连接协议的需求,许多选择Linux和Windows(我称之为OS汤)的许多选择 - 增加了坚固化,特定的形状和形式以及行业以及行业- 特定的功能和认证,例如1级,第2类,用于爆炸证明。

The sharp ramp in complexity at the embedded and control edge

当硬件变得如此限制以至于需要嵌入软件时,在嵌入式或控制边缘的复杂性有一个关键的拐点,从而失去了虚拟化和容器化的灵活性。另外,该软件需要一个实时操作系统来满足确定性响应需求,例如在工厂地板上的可编程逻辑控制器和车辆中的电子控制单元。我称这个拐点为薄的计算边缘,从那里到达设备边缘,复杂性曲线会急剧升起,直到您基本上为每个连接的产品构建自定义硬件为止。

软件一致性可以扩展到薄计算边缘

同时,软件复杂性曲线(表示为图1中的绿线)保持更长的时间,与从云通过电信边缘和本地数据中心从云到云的既定标准保持一致,直到第一个有效的颠簸发生在上述情况下发生OS汤。曲线继续保持相对平坦,直到您在薄计算边缘击中资源约束的设备。

该拐点是由总可用内存(而不是CPU处理能力)驱动的,如今,它通常约为512MB,足以容纳OS和最低集装箱应用程序集以实现有意义的目的。虚拟化和容器化提供的灵活性可以维持软件定义的灵活性,从云到所有薄的计算边缘都含有足迹税;但是,如果任何给定设备可以支持它,这是值得的权衡。最终,软件复杂度曲线与Extreme Device Edge处的硬件曲线达到了均衡,现在您也为每个设备创建自定义嵌入式软件。

边缘计算解决方案的关键注意事项

我们已经确定,硬件和软件在越接近设备边缘的距离越近都会变得更加复杂。当可用的内存成为约束时,软件保持更长的时间,一直到薄计算边缘,您必须嵌入。这是一些关键考虑因素开发边缘计算基础架构

Extend cloud-native principles, such as platform-independent, loosely-coupled microservice software architecture, down to as close to the thin compute edge as possible. In doing so, you can maintain more consistent software practices across more edges, even when you inevitably need to go more custom for the hardware. The opportunity to bridge the software-hardware complexity gap close to the thin compute edge with more consistent software tools is represented by the yellow bar in Figure 1. Further, abstracting software into individual microservices — such as discrete functions — as much as possible enables you to easily migrate workloads up and down the edge to cloud continuum as needed. For example, in an initial deployment you may start with running an AI model in the cloud for simplicity, but as your data volume grows you’ll find that you need to push that model down to a compute node closer to the device edge to act on data in the moment and only backhaul meaningful data for retention or further batch analysis.

利用开放式互操作性框架(例如Edgex Foundry)用于您的各种边缘计算部署。Edge X框架将云本地设计原理一直延伸到薄的计算边缘,提供了灵活性,同时还统一了围绕开放API的商业和开源值ADD的开放生态系统。此外,还将有嵌入式的商业变体将离散平台微服务压缩到基于C的小型二进制中,因此代码可以在高度约束的设备上运行或服务需要确定性实时的用例。灵活性和性能之间的权衡处涉及固有的物理学,但是即使这些压缩变体仍然能够利用Edgex生态系统中的大部分插入式增值,例如South-和South-and的设备和应用程序服务北部数据传输。在所有情况下,借助开放的供应商中立的Edgex API,您都可以通过第三方在更广泛的生态系统中编写的微服务更容易地发展解决方案。

确保您的边缘硬件适当稳健,以应对已部署的用例的物理世界的需求。30美元的制造商董事会非常适合替补席上的概念证明(POC)项目;但是,当您将其完全包装在较低体积的外壳中时,它的成本超过100美元,并且由于不打算用于这些环境,它很可能会在通常坚固的现场部署中失败。

Speaking of robustness, consider leveraging virtualization, automated workload management and orchestration tools and redundant hardware to provide fault tolerance in mission-critical use cases. Probably not something you’re going to care about if your edge solution is monitoring a connected cat toy, but certainly worth consideration if downtime in your factory costs thousands if not tens of thousands of dollars a minute.

通过I/O和计算功能来过度配置您在现场部署的硬件。只要您通过将云本地软件设计原理扩展到功能强大的边缘设备和部署的设备具有必要的物理I/O和计算净空,只要您尽可能地使用软件定义的技术,您就可以不断地更新现场的边缘功能您的需求不可避免地会随着时间的流逝而发展。如果您不将正确的I/O部署为防止未来,那么您将花钱在卡车卷上,通常费用高达750美元。换句话说,该制造商董事会的真正成本是多少?

Speaking of truck rolls, developers often overlook device management when启动一个物联网项目因为自然而然的是他们的第一个关注是他们的应用。重要的是要从一开始就真正考虑设备管理,不仅包括如何持续监控基础架构的健康状况,还包括如何在现场更新您部署的设备。当您在一到几个的聚会中进行POC时,很容易单独远程远程访问每个设备以通过命令行进行管理,但是尝试数千千万到数百万到数百万部署的设备。而您要做的最后一件事是用USB插入棍子上,手动更新设备。

Consider whether the infrastructure will be running on a LAN or WAN relative to the subscriber devices that access it. Note the break point in Figure 1. This makes a big difference in terms of tolerance for downtime in any given use case.

Modularize your hardware designs as much as possible, including with field-upgradable components. However, note that modularization can come with impact to cost and reliability since modular connections tend to be more failure-prone due to corrosion and vibration. In fact, it’s advisable to balance modularity with soldering down certain components – such as memory modules — on edge hardware that will run 24/7 in harsh environments.

确保您的边缘硬件具有适当的长期支持 - 通常至少超过船舶日期。这同时适用于硬件和可用的支持OS选项。

In general, plan on flexibility to address OS soup at thinner compute edges and both x86 and advanced RISC machines (ARM) based hardware. In Figure 1, the device edge is pretty much all ARM. This is another reason to leverage platform-independent — both silicon and OS — edge application frameworks.

确保将信任根(ROT)投资到硅水平。腐烂的硅(例如受信任的平台模块)使您能够确保设备证明它是它所说的,并且使用安全的启动,它正在运行应该运行的软件。此腐烂是任何良好防御的深入安全策略的基础。说到上述安全性可用性,英特尔和ARM在安全设备上的合作是一项重要的努力,旨在促进可信赖的后期所有权与多方供应渠道中的设备结合。这项努力正在蒸蒸日上,包括Fido最近决定启动物联网轨道并在内部进行安全的设备,从而在其内部进行首次标准化工作。

Stay tuned for the next installments of this series in which I’ll dig deeper into the edge topic with pointers on sizing edge workloads, my three rules for Edge and IoT scale and eventually how we scale to the grail.

所有的物联网议程网络贡献者都负责其帖子的内容和准确性。意见是作家的,不一定会传达物联网议程的思想。

SearchCIO
Search安全
Search联网
Search数据Center
Search数据管理