The Artificial Intelligence In Three phases To Autonomous IT

Artificial intelligence (AI) is permeating more and more areas. In addition to devices and machines, the new technologies now also support IT operations: They enable networks to operate autonomously, for example. For companies that want to take this path, the IT system house Circular Information Systems recommends an approach in 3 stages.

Chatbots answer customer questions, intelligent robotics control system maintenance, and voice recognition simplify the handling of mobile devices: AI is already used in a variety of ways. In IT operations, the new technologies can be found under the keyword Self-Driving Network. The advantage of the self-controlling network: The IT environment requires significantly less manual intervention. It also becomes more reliable and safer.

Collect and evaluate data in a targeted manner

The first thing to do is to know what is going on in the network. This requires meaningful data on devices and systems as well as on network and service performance – ideally in real-time. In some cases, monitoring solutions still determine this information via SNMP (Simple Network Management Protocol). However, with this method, the network load is very high and the recorded data is sometimes inaccurate. Accurate real-time performance monitoring can only be achieved with streaming telemetry. Because streaming telemetry is push-based. It continuously transmits reliable data according to defined guidelines from different sources such as routers, switches, or firewalls to a central platform.

AI already helps to speed up analyzes here – because administrators can ask their questions directly in natural language. “What’s wrong with my switch?”, “What was the performance of the WLAN network last Friday?” Or “How do my switch uplinks work?” Is an answer to an AI-supported solution in detail. It also provides help for troubleshooting. Furthermore, AI technologies support predictive analyzes. If anomalies occur, proactively notify the administrator. All of this happens before a user even realizes that there is a network problem.

Driving automation forward

Another prerequisite for a self-controlling network is extensive automation. Modern control software already offers extensive options for this, for example, to flexibly direct data flows and avoid bottlenecks. In the WAN, for example, this is already possible today with solutions for software-defined WAN (SD-WAN). Its cornerstones are intelligent data routing, zero-touch provisioning, and unified threat management. Administrators first prioritize the data traffic and the applications. The solution then routes the data intelligently in day-to-day operations by automatically and dynamically selecting the most suitable WAN transmission path. Zero-touch provisioning ensures that new devices such as switches or routers can be automatically put into operation at distributed locations. Unified Threat Management, in turn, acts as a protective shield over the entire network infrastructure. The focus of the administrators is shifting from routine activities to more demanding tasks: defining the set of rules and handling exceptional cases.

Also Read: The IT security Trends For 2021

IT learns to walk

In phase 3, the self-regulating network, the administrator hardly intervenes at all. An AI-supported network management solution learns from the collected and evaluated data. The implemented guidelines and automation then channel the implementation. If all these components are connected, the system learns adaptively and can control and optimize itself to a certain extent.

In this way, the system identifies the causes of problems in LAN or WLAN, among other things, and automatically initiates measures. An intelligent, self-learning solution such as the AI-supported Mist Systems platform from Juniper Networks, for example, adds missing VLAN configurations, corrects an incorrectly configured switch port, or adjusts the radio resource management of the access points required. If an AI engine cannot yet resolve an anomaly, such as a change in the bandwidth pattern or changed server behavior, it proactively notifies the administrator and learns from troubleshooting.

That brings it

Today’s network has to be fast, agile, and fail-safe. Companies can counter the increasing administrative effort with autonomous networks. These will increasingly set up, control, analyze and optimize themselves. It makes you safer and more reliable – because networks identify and fix potential performance problems before users even notice. But experience shows: The implementation of an autonomous network takes time and a sure instinct. Every organization has individual needs for which the right solution and strategy must be determined.