How is the user really doing? Users often notice restrictions, even though all lights are still green in the monitoring. NetEye captures the situation of the users in two ways: User inputs are simulated on the client to measure the real latency end-to-end. In addition, NetEye uses RUE (Real User Experience) to record the assumed average performance on the network not only as an average value, but based on a sophisticated analysis using complex statistical methods and machine learning.
Performance and workflows are consistently evaluated in NetEye with end-to-end user experience monitoring using powerful simulation analytics. This opens up a view of the actual user-perceived data on performance, availability and latency at client level. You see in detail and in real time how your users perceive the performance of IT. You recognize emerging bottlenecks early on, before they have a noticeable effect on the users' ability to work.
With NetEye ITOA (IT Operation Analytics) you can measure your existing resources such as CPU load, RAM usage or latencies at different levels such as operating system, database, application or network. Bottlenecks can be avoided via the Performance Problem Solving. The measurements also provide detailed data for estimating the cost-effectiveness of migrating individual services to the cloud. Cloud resources are analyzed precisely in order to immediately identify unused components and thus better exploit the potential for cost reductions in cloud use.
In addition to the pure visualization of performance metrics, NetEye allows to analyze the collected data by means of multivariate methodology. Machine learning algorithms are able to examine numerous time series simultaneously and interpret each individual timestamp as a state of the overall system. Based on this historical data, NetEye can make forecasts for the future and thus determine the probability of a failure occurring in a given constellation.
Imminent disruptions and attacks on the systems initially reveal themselves as anomalies in the network. NetEye is able to detect these anomalies with high reliability at an early stage. For this purpose, NetEye uses complex static methods that go far beyond the conventional, mean value-based approaches. Due to the graphical processing of the information, your administrators have the data traffic in the network under control at any time and can eliminate problems faster.
The demand for resources such as CPU, RAM, storage is constantly increasing. It is often difficult to estimate how fast the demand is growing or when new resources are needed in data centers or the cloud. Although modern technologies offer the possibility of adding resources "on demand", the costs increase accordingly. Forecasting in NetEye allows you to create models for your capacity management that accurately illustrate future demand, enabling better planning of resources and costs.
IoT and IIoT generate enormous amounts of data that are useful for gaining important insights and predictions. The data must therefore be processed in real time using machine learning and artificial intelligence (AI) methods. NetEye acquires data from IoT and IIoT sensors and aggregates them visually in clear dashboards, providing an in-depth view of the status of the processes connected to these devices.
In distributed application architectures and microservices, troubleshooting and solving bottlenecks is generally time-consuming and complex. Application Tracing can help identify exactly which lines of code are responsible for blockages, thanks to the Elastic APM technology integrated in NetEye.