With the help of artificial intelligence (AI) and machine learning (ML), predictive network technology alerts administrators to potential infrastructure problems and provides workarounds. 

AI and ML algorithms used in predictive network technology have become critical, says Bob Hersch, a director at Deloitte Consulting and a US leader for Platforms and Infrastructure.

Predictive network technology takes advantage of artificial neural networks and uses models to analyze data, learn patterns, and make predictions. AI and ML significantly improve observability, application visibility, and the ability to respond to network and other issues.

Although predictive networking technology has made impressive advances in recent years, many developers and observers are confident that the best is yet to come. 

The tools and systems are available now, but like most significant evolutions in technology, there are risks for early adopters, as development and assessing the effectiveness of a change are in flight.

Predictive analytics is no longer just for predicting network outages and proactively handling bandwidth and application performance issues. 

Predictive analytics is now being applied to issues surrounding the network. It helps address the downsides of SD-WAN, most notably the case of provider sprawl and the need for broader carrier service management and optimization. of telecommunications costs.

He continues that these “have become more important issues in the era of exchanging MPLS (single and two carrier services) for broadband services involving potentially hundreds of Internet service providers.”

Predictive Networks Advance

The most recent evolution of AI is related to the most crucial development in predictive network technology. Cloud-based AI technologies can improve the quality and speed of information delivered to network technicians while giving them a valuable tool for investigating outages and other issues. 

AI can detect anomalies faster than humans and even analyze the root cause of a monster, helping guide a technician to understand and fix the problem more quickly than before.

The integration of AI tools into predictive network technology also has the potential to be an economic game changer. “With mature AI and ML tools, service providers and organizations can reduce troubleshooting costs.  

In addition to the bottom-line economic benefits, AI helps simplify management, whether within a company or across a service provider’s portfolio.

Bryan Woodworth, the principal strategist for Solutions at multicloud network technology firm Aviatrix, believes that predictive network technology will advance rapidly in the coming years. 

It already helps to solve network problems quickly and efficiently. AI can correlate alerts and error conditions across disparate systems, discovering related patterns in minutes or even seconds, which would take humans hours or days.  

Predictive network technology can also drastically reduce false positives in log and error scans, leading to more competent and helpful alerts. “You can’t cure yourself of something you don’t detect.” For example, he adds, “Before you change the network to route a problem, you need to know where the problem is.”

Works best in data centers

Network behavior analysis examines network data such as ports, protocols, performance, and geo-IP data to alert when a significant change in network behavior may indicate a threat. 

In the future, this data can be fed into an AI model that can help confirm if the threat is real and then make suggestions on how to remedy the problem by changing the network,” says Woodworth. 

This type of predictive modeling works best within private networks, such as the data center, because [that’s where] humans have complete control over all network components and the data they generate.

The task becomes more challenging for public networks, including those connected to the Internet. Learning models must be designed to compensate for systems that are not under direct control or that provide incomplete data sets. This means that the learning models will make less accurate predictions, and humans may need to adjust them to make up for missing data.

To be fully effective, advanced AI and ML models must run at the production level and scale for bug fixes, Smith says. Decision-makers must trust the model results, and technology backers must run operations efficiently.

Meanwhile, continued advances in cloud technology and graphics processing units (GPUs) are taking modeling to new levels. 

Open source and commercial frameworks are helping organizations implement ML operations quickly and at scale with less risk associated with the time and complexity required to set up cloud and open source systems for AI.

Several of the leading cloud providers have already implemented management and optimization features of the AI ​​model. The technology can be found in tools like Amazon SageMaker, Google AI Platform, and Azure Machine Learning Studio. “Open source frameworks like TensorRT and Hugging Face retrain additional opportunities for model monitoring and efficiency.”

Workload analysis

By understanding your workloads—the network traffic they generate, latency requirements, and who is consuming data, how, and where—you can identify the high-fidelity data needed for predictive networking to support self-adaptive Virtual Private Clouds (VPCs). 

Micro-segmentation, load balancers, and traffic shapers help optimize delivery. The same high-fidelity data used for network-centric AI can supplement cybersecurity teams’ consolidated extended detection and response data lakes for security analytics.

Routers, wireless applications, switches, and other general network equipment do not typically collect user-specific data. While application performance monitoring tools measure user data, they cannot correlate the results with proactive network actions. 

Networks must be aware of users and applications to collect the data types required to build actionable models for AI and predictive technologies.

It is the future

The emerging field of neuromorphic computing, based on chip architecture designed to mimic the structure of the human brain, promises to provide highly effective ML on edge devices. Predictive network technology is so powerful because of its ability to receive signals and make accurate predictions about equipment failure to optimize maintenance.

Thus, he comments that neuromorphic computing will become even more powerful when it moves from predictive to prescriptive analytics, which recommends what must be done to ensure future results. And is that the chip architecture of neuromorphic computing is oriented to make intelligent decisions in the edge devices themselves.

Organizations like IBM, Intel, and Qualcomm are developing neuromorphic computing technologies. Some companies have released neuromorphic computer chips for research and development purposes.

These chips still need to be made available for general commercial use. It will likely take several more years of intense research and development before neuromorphic computing becomes a mainstream technology. Once it’s viable, the impact will be massive.


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