Is Network Monitoring Really Needed?
It is almost impossible to run a network and be unaware of the stuff happening in it. Phrase network monitoring implies all processes related to monitoring and analyzing the performance of the computer network, ensuring it runs efficiently and effectively. Process includes monitoring the performance of switches, servers, routers and other network devices, together with monitoring the usage of network resources, such as storage and bandwidth.
Currently, there is a plethora of different tools which are used in network monitoring. Here’s a list of some, very well known and frequently used:
- SNMP – Simple Network Management Protocol is a widely used protocol for monitoring and managing network devices. It allows network administrators to collect data on the performance of devices such as routers, switches, and servers.
- WMI Windows Management Instrumentation is a Microsoft technology which allows network administrators to monitor and manage Windows based devices.
- Nagios is one of the most popular open-source network monitoring tools, mostly for monitor network devices.
- PRTG Network Monitor is a commercial network monitoring tool used for monitoring a wide range of network devices and services.
For implementing network monitoring effectively, it is rather important to have clear understanding of what is the purpose of the network, and what kind of resources network needs, to support it. Also, it is crucial to understand the traffic patterns of the network, as well as applications and devices inside the network.
Network monitoring is essential in ensuring the reliability and efficiency of the network itself. It is a key for identifying and resolving issues before they produce significant impact and cause disruptions. Moreover, it allows network administrators to make decisions regarding optimizing network performance.
But, are the existing tools, everything we have?
What Will Future Of Network Monitoring Bring?
Currently the world is witnessing the expansion of artificial intelligence (AI) tools, as well as machine learning (ML), and it is next to impossible that these two didn’t find their spot in the world of network. It is true that one of the latest trends in network monitoring is the use of artificial intelligence alongside machine learning, using it to automate and improve the monitoring process. Such technologies are very helpful in analyzing large amounts of data obtained from network devices, in successfully identifying patterns and anomalies, as well as in making predictions about potential issues. All of those are extremely valuable to network administrators, since they make quick identifying and resolving issues, easier.
The second growing trend is the shift towards cloud-based network monitoring. This type of solutions allow network administrators to access network data and troubleshoot issues remotely, requiring nothing but internet connection. This provides great flexibility, scalability and helps reducing costs associated with on-premises solutions.
In addition to AI and cloud based solutions, the focus is also on monitoring and managing hybrid networks. This may include both cloud-based and on-premises infrastructure. The need for management and monitoring tools that can work across multiple environments is becoming more important with the fact that more organizations are adopting hybrid cloud strategies.
Artificial Intelligence In Network Monitoring: Is it just a fancy trend?
Artificial intelligence and machine learning are becoming more popular regarding network monitoring. The potential they have in implementation of automating and improving the entire process, is enormous.
Both artificial intelligence and machine learning, happen to be extremely beneficial in analyzing massive amount of the collected data. They are also rather helpful with identification of anomalies and with predicting potential network issues. Some of them are:
- network congestion issues
- security breaches
- different sorts of issues which could have great impact on the security alongside the performance of the network.
Anomaly detection: Is AI really that intelligent?
The anomaly detection in network monitoring is typically accomplished by the use of Machine Learning (ML) algorithms. Such algorithms are trained on large amounts of historical network data, learning the normal behavior of the network. Once trained, the algorithm is capable of analyzing new data in real time, together with identifying any deviations from normal behavior. This indicates potential anomaly or issue, and can send alert to network administrator.
Using AI for anomaly detection in network monitoring has several benefits. Ability to handle large amount of data from multiple sources is one of them. This can include data from all kinds of network devices, systems, as well as data from security tools.
Artificial intelligence based anomaly detection systems can be configured to provide different level od alerts. For example, if there is minor deviation occurring in network behavior, it can trigger a warning, but more serious deviation might trigger a critical alert. This allows network administrators, to prioritize their response to issues.
Another benefit of using artificial intelligence in anomaly detection is its ability to learn and adapt to changes in the network environment. As the network changes, alongside its usage patterns, algorithms can be retrained and adjusted to changes.
Performance optimization: Does it require a human to optimize network?
Beside anomaly detection, artificial intelligence in network monitoring has another use – performance optimization. It is becoming increasingly important in network monitoring, as organizations look for ways of improving the efficiency and effectiveness of their networks.
The primary way AI uses for optimizing network performance is through ML algorithms. These algorithms analyze collected data, which allows them to identify bottlenecks and other issues impacting network performance. With identifying this kind of issues, network administrators are able to take appropriate steps in resolving and improving network overall.
Through using predictive analytics, artificial intelligence works on optimizing network performance as well. Predictive analytics can analyze the historical network data which is essential in indicating future performance issues. With forecasting issues in advance, network administrators are able to take proactive measures to prevent or mitigate them.
AI based performance optimization can also be used for automating routine tasks. That includes capacity planning and source allocation. Using AI, system can automatically make decision about allocating resources, such as bandwidth and storage, ensuring that network operates at peak performance.
Troubleshooting network issues is another subject where AI comes in handy. It’s been already stated that AI analyzes data from multiple sources. This is rather helpful with identifying the root cause of the issue, as well as suggesting solutions.
Cloud based solutions: Everything goes remote?
The phrase cloud based network monitoring, refers to the use of the cloud based tools and services in order to monitor and manage the performance and health of the network. This include network devices monitoring (switches, routers, servers), network traffic and bandwidth usage monitoring, together with other key performance indicators monitoring.
Network administrators are very fond of this solutions – it allows them to access monitoring data and perform troubleshooting from anywhere with internet connection. This is very useful for organizations which have distributed networks and/or remote teams.
There are several benefits about cloud based solutions in network monitoring, including:
- Scalability. Cloud based monitoring solutions can easily scale to accommodate the needs of large and complex networks.
- Cost-effectiveness. Cloud based solutions are more cost-effective than traditional on-premises monitoring solutions, considering they typically require less hardware and maintenance.
- Automation – meaning that this solutions can automate several network monitoring tasks, such as data collection, analysis, and reporting.
- Reliability. Cloud providers usually have multiple data centers together with redundant systems in place to ensure high availability and minimize the risk of data loss.
- Security. This type of solutions is able to provide advanced security features, such as encryption and multi-factor authentication. Result is to protect network data and systems from any unauthorized access.
- Integration. It is possible for cloud based solutions to integrate with other cloud-based tools and services, like cloud-based storage and analytics platforms. This provides a more comprehensive view of network performance.
Conclusion?
The future brings all kinds of improvements regarding technology. Artificial intelligence, machine learning and cloud based solutions are already raising interest of all kinds of engineers across the world. These tools have found their place in almost every scientific field, so them offering solutions and improvement in network monitoring was not a great surprise. Analyzing humongous amount of data, possessing trained algorithms who are capable of identifying patterns and make predictions about potential issues, helping to quickly identify and resolve issues – that offers quite an improvement and optimization for network administrators all over the globe. At the same time, cloud based network monitoring provides several beneficial ways to monitor and troubleshoot networks, all while working remotely.
It is quite clear that AI has huge potential to be a valuable tool for network monitoring, and it is certain that it’s adoption will grow in the future.
