AI and the Future of Network Monitoring!

 The world of technology is constantly evolving and with it the potential of Artificial Intelligence (AI) and its uses in the field. By applying machine learning to examine data and historical trends, you can perform highly complex tasks while reducing human input.

This overview will describe the problem that AI and network monitoring must tackle, and how they both can do so.

The Problem

Over the years, humans have tried to figure out the solution to examining data and trends without the fear of human error. One of their earlier attempts was Dark Trace. While the software’s front end was astounding on the outside, the inside was abysmal. It took many hours of human input in order for the software to do its job of predicting and recognizing attacks and finding solutions. Despite it sparking people’s imaginations on AI, it wasn’t the AI solution that anyone wanted or needed.

Regardless of the failure of Dark Trace, researchers and developers have continued to dabble with AI. Today, AI can be used in network monitoring where large amount of time can get eaten up by keeping things as they are instead of looking for ways to improve your network performance. But what does AI and machine learning actually bring to network management?

Here are some unique ways that AI and network monitoring can work alongside each other:

Data Processing and Analysis

On a daily basis networks spit out large amounts of data that needs to be processed in order for you to understand the state of the network. This can be automated through AI and machine learning as the AI sifts through the data while comparing it to historical logs in order to identify potential trends. In most networks, a large amount of data is produced and rarely accessed meaning you may be missing key areas to improve the network performance and reduce its downtime.

It also has the added benefit of providing you with alerts real-time of any malicious or dangerous data as it occurs allowing you to focus on dealing with those issues instead of managing the data. “Human oversight is still critical here as while the AI can identify a problem it will not be able to determine what caused it,” cautions tech writer Katie Broomfield, Ukwritings review and Assignment service. It’s best to use AI to simply process the data and identify potential issues, then to take the time to have actual employees look into what caused the issue in the first place and implement changes.

One example of data processing and analysis at work is Service Delivery Intelligence (SDI) from Enterprise Intelligence. It’s an AI solution that speeds up performance analysis data searches, so that problems can be detected, and solutions can be made on the fly. It also allows for AI-assisted capacity planning, meaning that traffic flow can be simulated and then carry adequate switch-by-switch performance.

Problem Solving

In addition, there are some areas of problem-solving AI and machine learning can help in. It is possible to build smart switches into your network AI in order to manage the flow of traffic on your network and prioritising critical tasks. This means that you can be sure traffic is being managed in terms of priority without worrying that critical traffic is being held up in the system. The AI does this by analysing Ethernet packages to assign various levels of service while preventing compromise in the transition of other network data. This does require human input as you need to program the AI to understand what data should be prioritised and what should be given lesser priority.

Regardless, there are some reputable AI solutions that allow for great problem solving. For example, IBM Watson Field Service Advisor is an AI solution that helps technicians resolve field service requests without them having to scramble to meet them. IBM Watson will suggest possible solutions on how to resolve problems, providing “over-the-shoulder support” for technicians.

Human Input

Finally, let’s not rule out humans entirely.

“While no human can match the efficiency and processing capability of a machine, humans still remain crucial to inputting the data and understanding its context. AI can still give false positives, it’s up to us humans to identify when it happens,” says business writer Jason Stanley, Academ advisor and Via Writing.

We may be moving forward but without humans there is no use for AI which is not capable of making critical decisions relating to cyber-security and identifying the areas that are best suited for improvement. Automation is a good tool for improving the overall processing of your data but should not be the pillar on which your network is built and the be all and end all of network monitoring.

Conclusion

Overall, in terms of providing a solution for the general administration work involved in network monitoring, AI and machine learning is making great strides to automate this process allowing your team to focus on the areas that actually matter. However, it is not something that works by magic and not a silver bullet for your problems. You need humans to program and maintain the AI in order to make sure that it is doing what you want it to do, and there is no AI yet able to fully understand the context of a situation or identify the causation of any issues.

It’s worth investing in an AI to lessen the load on your networking team, but be warned that this may create some extra work in turn, especially when first getting set up. If you use it wisely, AI will bring great benefits and help you quickly deal with network situations as they occur.

The author - Technical writer and project coordinator Sara Sparrow, Best essay writing services and Student writing services, consults businesses and participates in conferences to share her knowledge of technology and marketing. In her spare time, she also writes on a wide array of topics for online magazines and blogs like Top essay services

Coin of the Realm

 

Your brain is a greedy little fellow. Although it comprises only 2% of your body weight, it consumes 20% of the oxygen supply. Deprived of oxygen for a mere one minute, it’s cells will begin to die off and two minutes later, serious permanent damage will have occurred. Oxygen is number one on Maslow’s hierarchy of needs – love, esteem and self-actualization all build on a foundation that starts with oxygen. It’s one of those essential things that we take for granted, until it’s at risk.

Scientists say that the typical adult human brain uses about 12 watts of power, obtained from combining that oxygen with glucose. This is a lot in relation to the rest of the body, but efficient compared with a 60-watt light bulb, and mind-bogglingly miserly next to IBM’s Watson, which requires around 750,000 watts. The brain’s energy budgeting prioritizes things we focus on and diverts its energy resources accordingly.

The actual workload of your brain can be divided into two main tasks – the inward facing job of maintaining your bodily systems, and the outward facing one of gathering and processing information about the world around you. We survive and flourish based on what information we gather, and what we do with that information. Modern technologies – Artificial Intelligence, Deep Learning, Machine Learning, and more or less everything else – are also driven by data. It is the starting point for countless innovations.

On average, individual humans create 1.7 MB of data every second. Our technologies create a lot more – we are surrounded by an ever-increasing sea of data. While there are only a few gigabytes worth of neurons in your brain, they work together in various ways to produce an estimated storage capacity of around 2.5 petabytes. Meanwhile, in a few short years, the total data repository around the globe is expected to reach 175 zettabytes.

As we move up Maslow’s hierarchy we eventually encounter less urgent things like food and shelter. Hunting, foraging, and building are all-consuming jobs, and some form of specialization and exchange makes a lot of sense. It also frees up time for the less tangible Maslowian needs revolving around self-actualization.

In the beginning, the exchange of choice was barter, still preferred by individuals and organizations as a way of trading goods and services. From cows to cowries to coins – and eventually to paper money – humans have used various means to exchange what they have for what they need.

While physical tokens still exist, digital cash in the form of electronic transactions - e.g. PayPal, Venmo, Automated Clearing House (ACH) – now dominate our monetary system. The connection to actual palpable money is blurred even further by the rise of Bitcoin.

Online search engines like Google and social media outlets like Facebook have increasingly come under scrutiny because of how they handle knowledge of our data gathering habits or our personal interests. These services were once naively believed to be free, although they exchanged the information they gathered about us with their customers for just over a quarter of a trillion dollars in combined 2020 annual revenue.

In 18th century England, the phrase “coin of the realm” was used synonymously with “legal tender”. The words “coin” and “realm” have since taken on more abstract meanings, and one might say, for example that “trust is the coin of the realm.”

The latest turn on the phrase is inevitable - data is the new coin of the realm.

Author Profile - Paul W. Smith - leader, educator, technologist, writer - has a lifelong interest in the countless ways that technology changes the course of our journey through life. In addition to being a regular contributor to NetworkDataPedia, he maintains the website Technology for the Journey and occasionally writes for Blogcritics. Paul has over 40 years of experience in research and advanced development for companies ranging from small startups to industry leaders. His other passion is teaching - he is a former Adjunct Professor of Mechanical Engineering at the Colorado School of Mines. Paul holds a doctorate in Applied Mechanics from the California Institute of Technology, as well as Bachelor’s and Master’s Degrees in Mechanical Engineering from the University of California, Santa Barbara.