As the Internet continues to become an integral part of our lives and work, the reliability of the connection is more important than ever. People may be okay with an occasional dip in speed, but downtimes could cost an ISP its customers. Fortunately, another transformative tech has been helping us with network quality and reliability assurance: Artificial Intelligence.
Here is an overview of how AI quality assurance technologies are helping companies ensure an unbreakable Internet connection reliability.
Limitations of Traditional Network Testing
If we want to truly recognize and appreciate the benefits that AI-powered network assessment brings to the table, we should understand the limitations of traditional network testing first. Here are some points that you may want to keep in mind.
Static, Rule-based Testing
Traditional network testing is heavily reliant on rules. It means it is very static and it uses scripts or text cases that are already pre-configured by a professional. It means that it cannot address or even recognize new kinds of issues that are not seen in the system before. Considering that a greater variety of systems are, sorry, threats are on the way, this is not a modern option anymore.
Reactive & Limited
There is no predictive element in traditional network testing either. On the other hand, it is minimal and reactive. It means the testing systems or their addressing system would kick in only when it detects a problem. Hence, there is no practical way to predict an outage and prevent it. The only option to address the issue is to detect a service degradation or lack of reliability after a massive downtime.
High on Human Resource & Intervention
Even though traditional testing systems may use scripts and many other programs, they require significant human effort, not only for writing scripts but also for running tests and getting insights. Not only that, but the aspect of maintenance also involves a lot of resources, especially when it comes to the scripts. This becomes a larger issue when the network systems also evolve and have to accommodate different traffic.
Less Adaptive
Continuing from the last point, these systems are not that adaptive to modern traffic patterns either. These days, most networks have to deal with a variety of traffic, including video, gaming, enterprise applications, the Internet of Things, etc. The problem is that traditional AI applications Testing limits its criteria to things like latency, back at low, jitter, etc. On the other hand, there are no ways to measure or quantify the quality of experience.
We must also note that these systems are heavily prone to human errors, both in scripting and addressing. Fortunately, AI helps in this regard quite a lot. Here are some brief pointers.
AI for Continuous, Real-Time Validation of Service Metrics
AI-driven network management uses surveillance, analysis, and automation technologies that can offer way better reliability for modern networks. One of the ways is through its continuous and real-time functionality.
Rather than activating upon predefined triggers, AI-powered network testing relies on real-time testing and automation. Because AI can monitor live traffic and take proactive actions, it retains an overview of the given network at any point. More importantly, this real-time functionality is essential while dealing with networks like 5G, IoT, and cloud-native systems. Therefore, the continuous nature of the testing assures us.
Real-time validation of the service metrics is also a point of improvement here. These AI systems also use Machine Learning to dive deep into classical data such as latency, jitter, CPU load, and bandwidth data. Also, because these systems can correlate these data points, the validation provides more insights and fast action. You can also count on AI when it comes to detecting anomalies or deviations in patterns.
In short, AI-driven network monitoring focuses on aspects that a trigger-based system may de-prioritize. It’s also a significant step towards Quality of Experience (QoE) testing. For example, AI validation gives more value to whether the end-user faces issues in streaming 4K video instead of merely stopping at ensuring that the ping is less than 10ms.
Predictive and Proactive Network Assurance
It is already clear that AI-based network testing and assurance give you a better way of looking at resources and handling issues, rather than relying on triggers and pre-configured scripts. But it does go beyond those elements to ensure that there is better network reliability. Two of the ways it does this are through predictive network assurance and proactive network assurance.
Predictive Assurance works through a combination of artificial intelligence and machine learning, which can go through years or decades of traffic data patterns. It can thus use this historical data to ensure, sorry, to forecast issues and threats before they occur. This is related to symptom monitoring because by looking at how threats have taken place in the past, these systems will be able to predict issues.
For instance, there might be an increasing latency in the network before there is congestion. It can also look at anomaly forecasting, such as degradation at the hardware or software level. But this prediction itself makes a huge deal because you’re not just dealing with the issue after the fact, instead trying to predict to avoid downtime.
Proactive assurance deals with remediation before the issue occurs or before the user notices the problem. Let’s say there is a latency-related issue that the AI has predicted, and it may have to do with the network congestion that is on the way. So the system may decide to reroute the traffic or allocate additional resources in that particular area so that the congestion never happens.
Sure, the system may require additional resources in this regard but this is way better than facing a downtime at the user end it may also work in other cloud-based services for instance if the system can predict a latency spike in the CDN nodes it can easily resource i mean allocate more resources so that the congestion never happens resulting in better load times not just for the network but also for the websites.
In many ways, an AI-powered network quality assurance system becomes a self-optimized entity instead of letting rules dictate how it reacts.
AI Applications Testing for Network Performance
AI-powered systems also go deep into application-level performance to understand how the end-user feels about the experience.
Compared to traditional testing scenarios, AI-based systems can offer a more innovative, faster, and adaptive way that understands what the user is going through instead of merely giving you a service metric. For instance, instead of relying on brute force testing, it basically uses multiple simulations to understand how the varying traffic situations will affect the KPIs.
For instance, if it detects an issue in user behavior, such as traffic spikes, the system may do something about the resource allocation so that the particular application, such as voice over IP or video conferencing, would work fine. It can also understand user behavior patterns and how individual applications will be affected. For instance, if there is a drop in latency, there will be issues in how Zoom calls will work on that network.
Wrapping Up
In short, the improvements that an AI-based network monitoring and quality assurance system brings to the table are more of a necessity for the varying types of traffic and the modern network architectures that we are coming across these days. And all these features, be it predictive analysis, proactive remedial systems, or even automated resource allocation, would become essential in the years to come as we embrace the internet even more so.
