Businesses today need highly flexible and easy to scale IT infrastructures to deploy new applications to meet changing market conditions quickly, incorporate new technologies (e.g., AI, 5G, Wi-Fi 6, and more), meet increasingly demanding user expectations, make use of new and growing datasets, improve operations, and deliver better services. Increasingly, IT organizations are deploying technologies that take manual work out of the picture and replace them with processes that automate network and systems control.
We’ve put together a collection of the best Network Computing articles on this topic. You can get the bundle of articles in a single report here. The articles include:
On the Precipitous of a Network Management Revolution
Network management is undergoing a transformation. Its function is rapidly shifting from one of keeping the lights on to playing a critical role in the way modern businesses achieve success. Driving this transformation is a perfect storm of factors: IT departments must do more with less; companies need to rapidly incorporate innovative technologies into their networks to deliver new and better services; and businesses must develop, deploy, and support new applications at a rate never before experienced in the industry. Making matters even more challenging is that these things are going on at a time when most companies are undergoing a digital transformation.
At the heart of these issues is the need for robust network management to ensure the underlying infrastructure that supports an organization’s business strategies is available and delivering optimal performance. Network management solutions also must automate many tasks that have traditionally been done manually. Automation can help an IT staff work more efficiently, reduce errors, and ensure optimal performance in complex networking environments. Ultimately, modern network management solutions will complement automation capabilities by adding intelligence to improve network operations further.
Read the entire article here.
Modern Network Monitoring is More than Just Utilization Stats
AIOps platforms combine traditional monitoring tools with streaming telemetry and data packet inspection and analyze all of it using AI.
There are tools available that network administrators can tap into to achieve the level of cloud, device, and application performance visibility required. Some of the data sources, such as Netflow and IPFIX, have been around for years, but often ignored from a monitoring perspective. Other monitoring data sources are new, including streaming network telemetry and network-based deep packet inspection (DPI). Thus, old network monitoring sources combined with the new are providing entirely new levels of visibility.
While added monitoring and alerting capabilities are great, it can add to the workload of an already busy network admin. That’s why we’re seeing a shift away from separate network, application, and device monitoring tools towards what’s being referred to as artificial intelligence (AI) for IT operations or AIOps for short. AIOps platforms combine traditional monitoring tools with streaming telemetry and DPI and analyze all of it using AI. AI analyzes each data source and correlates multiple anomalies to automate the identification of problems while also providing detailed information on how to fix the issue. Thus, if an AIOps platform is properly implemented, not only does it provide more visibility into potential problems, it also eliminates many manual troubleshooting and remediation tasks.
Read the entire article here.
What is Network Agility?
Network agility can be defined as the speed at which a network can adapt to change while maintaining resiliency, security, and management simplicity.
Within this construct, network agility is comprised of the following categories:
Network automation: One way to increase network agility is to leverage automation for the handling of processes that were previously performed manually. Automation can be used to assist with overall network performance and efficiency. Improvements can be made using intelligent data flow mechanisms. These mechanisms use network telemetry data, health probes, and AI to analyze application data flows and the various paths they can be sent over. Automation can then leverage the analytics processed through the AI to choose the most efficient path based on the criticality of each individual data flow.
Deployment speed and scalability: From a deployment standpoint, both speed to deployment and scalability are key areas that are addressed through network agility. The use of zero-touch provisioning and centralized control-plane architectures are two examples where speed of provisioning new network segments and services can be enhanced. Then looking at scalability, automation can once again be put to use alongside virtualization. Automation can be used to create and deploy pre-defined network templates that can be deployed with just a few clicks. The result is the deployment of a network using virtual network appliances and network functions that are deployed with uniform network policies throughout the private LAN and into the public cloud.
Network visibility: The best way to maintain long-term network agility is by having the proper level of visibility into a network from a data flow perspective. Deep data insights provide a granular view of the end-to-end operational health of a network. This level of visibility allows network architects to understand better what will happen when changes to network flows are disrupted, change, or are added to. Legacy network monitoring tools such as SNMP, traceroute, and ping are no longer enough if your goal is to build an agile network. Instead, modern network analytics platforms that source streaming network telemetry data and analyze it using AI is a far better choice.
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AIOps Tackles the Data Conundrum
Infrastructure complexity and data growth are undermining performance and availability. AIOps provides a solution.
Businesses are turning to artificial intelligence and AIOps (Artificial Intelligence for IT operations) to prevent or resolve high-severity outages and other ITOps problems more quickly on hybrid, highly decentralized, data-driven infrastructures. AIOps doesn’t flinch at a deluge of data – it thrives on data.
But what does AIOps do? An AIOps platform automatically learns the “normal state” of each critical business service and the underlying behavior of the supporting hardware and software services, making it easy to automatically flag anomalies. Using big data analytics, machine learning, and other artificial intelligence technologies, it can then automate the identification and resolution of infrastructure issues ranging from performance or availability issues to an all-out outage of the infrastructure.
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