Five network management trends for 2022

With the advent of the Internet era, people need to establish closer connections with the outside world, and then Wi-Fi becomes popular. Today, companies need to provide more powerful wireless LANs and help remote employees establish good network connections, so edge networks are around us.

Here are some of the key trends in network management:

Edge Network Proliferation

Edge networking is one of the hottest areas of networking right now. A study on the edge internet economy shows that by 2030, the edge economy will be worth $4.1 trillion. This has a huge impact on network management. Not only does IT have to manage LANs, Wi-Fi, remote connections via VPNs and other traditional responsibilities, it must also be able to monitor, maintain and manage the many edge devices and small data centers that are expected to emerge.

For example, telecommunications companies are investigating the possibility of combining edge data centers with their cell towers to be able to send data to local and central points, aggregate it and store it for a period of time. Self-driving cars will also require a large number of edge services to be able to monitor the myriad factors required for safe and efficient transportation. In addition, there is the Internet of Things (IoT) to consider. Because of the IoT, tens of billions of new connections are being added to the global network. This means a huge demand for edge processing.

The need for partners

As networks become more complex, the edge continues to evolve, and the IoT gathers momentum, it’s becoming increasingly clear that no one company can do it all. As a result, network management vendors are partnering with edge and IoT experts. IT vendors are building closer relationships with network vendors as they try to prepare for the demands of evolving network architectures. Enterprises are looking for help from everyone who can integrate edge, IoT, and other data into their operations.

According to a study by an agency from 2021, 20% of business leaders are already looking for suitable delivery partners for IoT and edge services. This number has increased significantly over the past year.

Web Hosting

More and more enterprises are creating custom services using third-party infrastructure. These hosted functions available as services are orchestrated into specific business logic. For example, enterprises can access a Docker registry that contains many software functions packaged as containers, which will be orchestrated into working applications using Kubernetes. Since there is no specific device associated with the service, the network management system has no idea about the availability of the service. Therefore, the network management system will have to deal with the challenges of these hosted networks.

Moving away from device-centric management

Traditional network management systems are built primarily to handle device management functions and how to provide services to these devices over the network. Services are provided by devices and are available as long as the devices are up and running. But this long-standing approach is being disrupted by trends such as virtualization, software-defined networks, cloud computing, and edge computing.

The relationship between services and devices is being decomposed, and existing network management frameworks must change as new frameworks are developed and implemented that span different layers of the stack and provide coordination across multiple domains in a multi-cloud environment. Solving this problem requires understanding how hosts and connections work and how to use them to create new services, which leads us to rethink network management as service orchestration.

As a result, network orchestration vendors are seeing increasing interest in their products. In addition, many of these vendors, as well as traditional network vendors, are working to drive the services market and bring new types of network management to the market.

AI and analytics-driven network management

Artificial intelligence is being incorporated into more and more applications and workloads. But it faces a major network problem. It takes a long time for AI systems to connect with edge data. If the architecture is too centralized, it may take up to an hour for data to be processed locally, transferred to the central AI system, analyzed, and then the findings or instructions are transmitted to where they are needed.

This approach may work when processing sales forecasts for the next quarter, but it fails when real-time workloads such as self-driving cars, financial fraud systems, etc. appear. The network changes required are to enable data to be processed and analyzed as it is created, rather than having to be transmitted to a central data center. This means edge data centers have a lot of processing power. ​