In 2018, operators began to deploy 5G networks globally, which is now in full swing. Compared with 2G/3G/4G, 5G has a significant leap in key performance such as network speed, network latency and connections scale, which would allow it to support new service scenarios and applications. To support the typical 5G service scenario, eMBB, mMTC and URLLC, and guarantee the network performance, various new technologies including Massive MIMO, uplink and downlink decoupling have been adopted. These new technologies improve network performance significantly but also increase the requirement of network agility and network complexity.
Recently, networking has become the focus of a huge transformation enabled by new models resulting from virtualization and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and more recently, edge cloud and fog. This development towards enhanced design opportunities along with increased complexity in networking as well as in networked applications has fueled the need for improved network automation in agile infrastructures.
Humans and manual processes can no longer keep pace with network innovation, evolution, complexity, and change. With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect.
As we step into the 5G era and new services and applications continue to emerge, new network technologies and features are being adopted; the traditional network management model is no longer sufficient to support the growing network operation requirements and to guarantee user experience. Also, the ever-increasing complexity makes it challenging to improve operational efficiency and control opex costs effectively. The industry has recognised that a highly intelligent network built upon AI technologies required in the 5G ere.
This new networking environment calls for more automation, as exemplified by recent initiatives to set-up network automation platforms. This can be combined with Artificial Intelligence techniques to execute efficient, rapid, trustworthy management operations.
Higher levels of automation are the only way to handle this complexity, while at the same time, ensuring that network resources are utilised more efficiently than ever, to reduce operating expense (OPEX) and support rapid, agile response. In addition to automation, operators also need simplified processes to reduce cost and increase agility to handle the ever-more complex network.
Artificial intelligence is made up of 3 principal branches, big data, automation and artificial intelligence. Big data gathers large data sets on which analytics are applied to gain insights and enhanced decision making. Automation is where machines follow pre-programmed rules to run processes, generally used for repetitive tasks. The final area is most advanced – Artificial intelligence where machines perform cognitive functions similar to those attributed to humans. AI algorithms take decisions as a consequence of the application of advanced analytical techniques and may be applied in combination with automated advanced feedback loops to solve problems. Artificial intelligence can be further defined by the application of learning that may be undertaken; machine learning and deep learning.
Major operators are in the game as AI creates further opportunities to pursue digital transformation for two core business areas (network operations and customer experience) and provide new services to enterprise customers. Driving greater network automation and digitising customer interactions are the dominant use cases in early AI deployments. Some operators are also leveraging AI to launch new products and services (digital assistants and smart speakers) and platforms (AI-as-a-service). Generating revenues in these areas will depend on the ability to strike the right partnerships, expanding ecosystem presence.
Cisco has debuted a series of software enhancements designed to put AI and machine learning deeper into the network. Key features include new network automation and analytics tools that are meant to help enterprise IT teams glean more insights and visibility from network data. Cisco is also touting new machine-reasoning algorithms for improved troubleshooting, giving IT admins and network engineers the ability to detect and correct issues and vulnerabilities more quickly. Moogsoft uses ML to correlate network events. Splunk has a similar system.
ML may be a technology that helps us proactively identify and correct problems — and maybe even help us get to the point where our network management systems can predict what a network is going to do.
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