IT monitoring trends. New technologies

What do you need to do to prevent DDoS attacks or a sudden increase in traffic from becoming a problem for your business? 97% of companies have experienced unexpected IT system failures. Hardware or software problems lead to errors, hacks, and financial losses.

Practically every day there are new devices and technologies that need to be constantly monitored, large infrastructures and hybrid cloud environments need to be supported. Coordinating servers in different locations, Amazon, Azure and Google clouds in one place is not easy.

Umbrella monitoring
Slow response from multiple servers, an incorrect attempt to access a third server, and abnormal CPU utilisation can indicate a hacking attempt or attack.

Umbrella IT monitoring allows different devices, applications or networks to be monitored in a single system. These systems do not connect to the objects themselves, but receive data from infrastructure monitoring systems. Umbrella systems perfectly cope with the tasks of collecting, storing and analysing information.

Intermediate proxy servers allow monitoring devices in inaccessible networks. They are used to scan networks, monitor nearby devices, and collect data.

Predictive analytics
Monitoring systems collect and process huge amounts of data. Long ago, no one looks for outliers manually, taking into account all correlations and nuances. Machine Learning (ML) allows you to combine information from different sources, compare data and identify patterns. Predictive analytics not only helps to understand the current state of affairs, but also predicts likely scenarios.

Emat EOOD uses well-proven machine learning models of predictive analytics - anomaly detection and trend prediction. Anomaly detection models look for anomalous and atypical values in systems, while predictive models are able to predict how data and metrics may change in the near future. This allows you to know about a problem in advance and prevent an incident - an unscheduled service interruption or quality degradation.

Automatic correction of trigger thresholds
Automated alerts and triggers help you respond quickly to changes in the state of your infrastructure. They help detect anomalies that are usually difficult to trace when monitoring static thresholds.

When working with customers, Emat company specialists use machine learning and analytics to analyse historical data and current trends to automatically adjust thresholds based on changes in the infrastructure. The system itself adapts alert levels based on the current operation of computer systems for various metrics such as CPU load, memory usage, disc space and network traffic. This allows monitoring systems to respond more accurately and dynamically to changes in load and application behaviour, minimising false positives and providing rapid notification of potential problems.

Scaling infrastructure
Scaling up legacy services and adding new servers, expanding cloud resources or using containerisation technologies to efficiently manage resources are all quite popular trends at the intersection of monitoring systems and capacity management systems.

Machine learning technologies analyse capacity consumption history and then assess how quickly a system or application can increase or decrease its capacity to adapt to changing demands and loads.

IT monitoring is useful for both network equipment (routers, servers, printers) and specialised devices (e.g. sensors or IoT elements).

Data consolidation
To ensure that customer systems are highly productive and secure and adapt effectively to changing demands and workloads, IT companies and their professionals must monitor cloud services, microservices, orchestrators, serverless computing, managed services (SaaS) and managed platforms (PaaS) on a daily basis. Any failure is a potential loss. It is important not only to react quickly to problems, but also to prevent them from occurring.

Emat EOOD Bulgaria uses artificial intelligence (AI) and machine learning (ML) to develop software that can process large amounts of data, detect patterns and anomalies, and prevent and avoid system and application failures.