An Easy Guide To Understanding How Autonomous Networks Work
As technology evolves, we’re presented with new ideas on how to do things better. Businesses of every size are always looking for new ways to improve their output, and many of them have come up with innovative methods of streamlining their processes. As the success stories pile up, there is one type of technology that has started booming recently – Autonomous Networks. And while they may sound like science fiction, everyone from small businesses to big corporations is starting to adopt the technology. In order to determine how that can be beneficial for your business, you first need to understand what an autonomous network is, and how it works.
1. What Are Autonomous Networks?
In a nutshell, autonomous networks are computer systems that can function without any human intervention. In other words, a zero touch network has the ability to monitor its environment and adjust itself accordingly. It is a process that combines cutting-edge artificial intelligence (AI) technology with deep connections and root cause analysis to identify abnormalities and provide independent remedies. This kind of network is a monitoring system of sorts that observes, engages, and acts with little to no human intervention. While the concept is still in its early days, and some human input is still required, the way it’s developing holds the promise that pretty soon, it will not only be able to perform anomaly detection but that it will be able to provide solutions and remedies all on its own. This is made possible by the advancements in the unsupervised machine learning algorithms that have been on the rise for some time now.
2. Web Design And Autonomous Networks
One way to explain how autonomous networks work is to look at them in the context of web design. While they may involve aspects that are not traditionally associated with website design, such as AI and data analytics, their entire function is built with the specific goal of making the design process easier for humans. In essence, they allow you to make your internet presence truly yours, which leads to increased engagement with your audience. Here are some ways that autonomous networks can contribute to the design of a website:
- Personalized User Experience – The more time people spend using a platform or service, the more engaged they become. With autonomous networks, you can ensure that every user has an experience catered to their individual needs.
- Content Curation – The more content you have, the better it is for your business. No one wants to visit a website with sparse content, and that’s why reliable networks can curate great content from trustworthy sources.
- Social Media Management – Facebook pages and Twitter profiles are all well and good, but as they grow bigger it becomes more difficult to keep up with everything that’s happening. With autonomous networks, it becomes easy to monitor social activity and keep your presence active across the web.
While the main focus of end-to-end networks is back-end activities, they mate to provide optimization to any design stage. With efficient networks guiding your hand, you can make sure that every element of your web presence is perfectly suited to what you do.
3. Anomaly Detection
The first stage in how autonomous networks function is anomaly detection. It enables real-time monitoring of all network data from connections, devices, radio networks, current and legacy core networks, services, transport, IT operations, and any other source. This is already a major step ahead of many existing CSP monitoring solutions that monitor data from a predetermined list of devices and applications. By constantly monitoring all possible data points in a network, end-to-end networks constantly work to ensure that both the infrastructure and its individual components are healthy. This means being able to discover abnormalities in real-time, instead of waiting for them to escalate into something more significant or potentially damaging. This has the potential to prevent downtime, security breaches, system failures, and other problems.
4. Root Cause Analysis
Fully automated baselining, in addition to a comprehensive view of the network, incorporates fully autonomous baseline-building that adapts to changing seasonalities and responds instantly and dynamically. The scope of data is examined in its entirety by using adaptable algorithms that take seasonality, trends, and other things into account. Anomalies are detected faster and false alarms are reduced to a minimum as a result of monitoring the whole picture with adaptable methods that take all the behavioral variants into account. Correlations provide the broader context of what’s going on, allowing teams to get to the core cause of every problem as quickly as possible while minimizing time and cost. The use of ML in network intelligence solutions allows them to correlate billions of metrics and related events and anomalies across multi-technology and multi-vendor networks. If CSPs are to effectively determine the probable source of acute issues such as outages, service degradation, and slow leaks, correlation analysis across multiple event data sources is essential. The use of AI can enable early system identification by correlating multiple inputs (performance, behavior, usage, device type) overtime to find changes in the network that could indicate major or minor issues.
ML-based monitoring is paving the way for automated remediation by automatically identifying network abnormalities and mapping their connections. These automated, closed-loop procedures may presently be observed in low-level operations like automating “bounce the server” or a “create a ticket” type of script. A script is run to correct the anomaly, which is closed when it’s resolved. This is great for low-level errors, but automated networks are already looking at taking this further with big data analytics and value-added services that can not only catch problems early but offer automated solutions while still in real-time. By being aware of the “normal” state of a network, they can easily identify issues as they arise and provide automated solutions to them.
6. The Long-Term Benefits Of Autonomous Networks
If an autonomous network is able to monitor its environment effectively in real-time, then it can also learn from that data. What this means is that when anomaly detection triggers a response to an event, the network can then use that data to improve its operating efficiency and – just as importantly – better predict future problems. By continuously improving itself over time, an autonomous network becomes more efficient and reliable with every iteration. For any business that relies on its network to provide services and stay profitable, this is a hugely beneficial long-term prospect, since it means reduced downtime and more time spent on growing the business as opposed to just keeping it up and running.
The benefits of fully automated networks for CSPs are clear: increased performance, reliability, uptime, and security all come from better monitoring and management of the backend infrastructure. This makes it easier than ever before to ensure that your network is operating efficiently and that you’ll know of any problems before they even happen.