The 21st-century enterprise that was already highly distributed and virtual became even more so last year when the COVID-19 pandemic triggered the world shift to remote work and distance learning. the basic way employees and students connected to at least one another changed overnight, generating profound impacts. Technical glitches disrupted operations and exposed security vulnerabilities.
In organizations where the bumps were more pronounced, IT quickly learned that performance, stability and security trusted visibility across the network. which visibility had to increase beyond the four walls of the normal office to wherever the traffic might flow, including headquarters, on a third-party cloud or the top user's home network.
Just enter cloud-based network monitoring tools, as those all expose the activity across a distributed network. Because they are cloud-delivered, they'll be deployed quickly and managed easily. Cloud-based monitoring not only delivers real-time network health status information, but it also logs historical performance, providing statistics IT can use for proactive optimization and forensic studies. it's prioritizing investments in cloud-based network monitoring to optimize performance and identify anomalies indicative of threats.
Traffic insights key to monitoring
The COVID-19 pandemic brought home the necessity to illuminate network activity, particularly as cyberattacks escalate. Ransomware attacks, as an example, rose 20% in 2020, per IBM's X-Force team. Data theft overall quite doubled between 2019 and 2020.
Network monitoring relies on a range of approaches to trace traffic patterns. Passive collection uses existing protocols like NetFlow and sFlow and log files to gather network traffic data. Synthetic monitoring generates test traffic automatically on an everyday basis, or on demand using software agents. Synthetic monitoring includes traceroute tests in addition as more dynamic variations to simulate real activity on the assembly network.
Polling is another technique, where performance data is collected from network devices via Simple Network Management Protocol or another method and so sent back to the monitoring provider, which analyzes the results.
IT learned that performance, stability and security relied on visibility across the network. which visibility had to increase beyond the four walls of the normal office.
Packet inspection provides the simplest way to parse and analyze packets captured from the network or from switch ports. Deep packet inspection provides a good more detailed picture of performance, capturing packets in transit in real time then mapping them against a collection of libraries characterizing different applications.
Traditional network monitoring focused exclusively on data collection within the firewall, but tools like synthetic network path tracking gave IT the power to observe activity across the complete environment.
Advanced tracking brings new capabilities
Effective cloud-based network monitoring doesn't stop at the wire. it's also important to look at the endpoint via software-based agents that capture stats from end users' machines. this permits companies to realize a perspective of their entire environment, and it's essential when determining if a performance issue is because of a problem on the network or isolated to the endpoint.
As with other areas in IT management, AI and analytics have become all the way all the way down to play a greater role in both real-time diagnostics and predictive maintenance. While that few IT administrators question the maturity of AI and analytics of it, these tools are emerging as viable options for anomaly detection via machine learning, smart log parsing and correlation.
Ultimately, AI and analytics are often used for real-time diagnostics and longer-term analysis to enable IT to manage components proactively before infrastructure problems occur. AI is probably visiting commence a new generation of cloud-based network monitoring use cases within the years to return.
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