The way we manage and monitor networks is morphing.
被动，无功工具正在被更积极的网络分析系统所取代，使整个网络团队提供关于网络行为的单一真理来源，并且对基础设施问题隐藏的位置更深入了解以及对他们有关的事情。
Before IT was forever changed by the arrival of mobile devices, virtualization and cloud apps, fixing network problems was relatively simple because users plugged into the network from one location to access local applications and resources.
But with the proliferation of diverse wireless clients – a range of hardware using different versions of different operating systems (the permutations can quickly scale into the thousands) – and the use of applications and services that are often not under IT’s control, getting to the heart of individual user and systemic client network problems has become the new nightmare.
This is particularly acute in the access network. Just think about a single user connecting to the network. The first leg in the journey is contending for Wi-Fi access, where various problems can scuttle or degrade the effort. Next the user must authenticate to the network, obtain an IP address and resolve DHCP requests, all of which can be problematic. And finally, they access applications locally or traverse a wide area link to the cloud, introducing still more opportunities for things to go south.
A hiccup with any individual step can result in service disruption and poor performance, but the user just chalks it up as a problem with “the network” (and probably as “a problem with IT”).
但是因为它，是一个设备操作系统问题吗？Wi-Fi问题？DHCP？ARP？DNS？应用程序失败？一个WAN问题？如果您能找到答案，而无需通过各种供应商管理系统生成的日志数据，数据包捕获和漂亮的屏幕和图形的卷，而无需刷新
Because that’s the reality in any IT shop today: boatloads of discrete vendor tools to troubleshoot individual products in specific parts of the network.
That model is no longer tenable due to the amount of data that must be gathered and correlated and analyzed to optimize performance. Blimey, everyone on the team needs to be a data scientist.
大数据分析，结合云计算和机器学习，正在推动一类新类基础设施网络分析的出现，该分析探查了遍历网络的数据，从而得出了网络和附加设备的健康状况的整体视图和服务和应用程序，从用户的角度来看。
这将从根本上重新定义它如何变得更加积极主动。
Not exclusive to any single vendor, the new class of solutions focused on this approach has been coined “user performance management” (UPM). Although still in their infancy, UPM platforms promise to fundamentally alter the traditional reactive workflows of IT.
Think Netflix for networks
了解监控解决方案与分析解决方案之间的差异非常重要。考虑netflix如何工作。当您观看电影或电视节目时，Netflix正在学习您保留的历史观看数据可能会感兴趣。然后，表明你可能想在没有你的情况下观看。这是分析。
Now try applying this analytics concept to enterprise access networks.
The network analytics platforms collect wired packets, device data, wireless metrics, applications and WAN data, and crunch all this data concurrently to understand important patterns and trends impacting user network performance from virtually any vantage point.
基于新的分析的解决方案在网络的所有部分持续凝视，在历史上历史上和历史上存储此数据以识别出现的较大模式和趋势，观看和学习客户端交易的行为。
这些系统是独特的，以帮助它更加积极，通过学习网络的不同部分如何工作（或不工作）。然后，他们建议，Ala Netflix，它该怎么做，提供网络管理员的配置建议列表，用于修复在整个基础架构中影响用户性能的特定客户事件或系统基础架构问题。
Most infrastructure vendors, especially those that have Wi-Fi products, are beginning to have an early version of network analytics. Advanced analytics approaches now coming to market from new upstarts are among the most sophisticated vendor-neutral systems in this space. With highly scalable cloud-based platforms designed to crunch massive volumes of data and correlating it across different dimensions, these new solutions figure out what’s actually going on. In turn, remediation recommendations can be automated and surfaced, often times, before network staff even realize there is a problem.
网络内的客户端操作系统在网络中表现不佳？什么网络服务超载或缓慢响应？什么Wi-Fi接入点提供覆盖范围或性能差？哪一个对用户性能产生最大的影响，并且应该首先固定？
Answering such questions proactively with a new generation of network analytics technology is the holy grail for enterprise network staff and represents a major leap in transforming network operations from a cost to a profit center.