intelligent networking Machine learning (ML) is a popular buzzword, including relative to networking. But is ML real technology we can use?
Understanding Machine Learning
ML is a small subset of the artificial intelligence (AI) field. A system is determined to be artificially intelligent if it can use the same observations as a human to arrive at similar decisions and take similar actions. An ML system, however, isn’t as intelligent. It’s simply a system that learns to identify specific patterns. The most well-known ML system, built by Google , has been taught to recognize cats in pictures. In healthcare, ML systems are being trained to identify tumors in radiological scans. A related technology, deep learning, uses interconnected layers of neural networks, resulting in the label “deep.”
On the humorous side, Janelle Shane, a research scientist, has been using ML systems to create mixed drink recipes , cookie recipes , and drawings . Her results are educational, showing what can happen if the learning corpus is relatively small or if the corpus is not a good match for the desired solution space. Look at the difference between a neural network that has to learn to spell words versus one in which the smallest quantity is a correctly spelled word. Her examples provide us a way to get a feel for how these systems work.
How can we apply this technology to networking, and specifically to network management? We’re seeing vendors starting to develop ML systems that can identify common networking problems. Moogsoft uses ML to correlate network events. Splunk has a similar system. Gartner has a name for the space: Artificial intelligence for IT operations, or AIOps. The most important step for any of these systems is to train it with enough data of the type that we want it to analyze and recognize.
ML’s learning process depends on getting input on what it is observing. The cat pictures experiment was successful because data scientists taught the system by showing it pictures of cats — millions of cats — all identified by humans. It could then recognize cats in a previously unseen picture. A similar process must happen with network management data.
Training an ML System
We have several problems to overcome when we try to apply ML to network management.
Sufficient Volume of Data
To be able refine the weights a network uses internally, ML neural networks require very large volumes of data. The volume of data from one network may not be sufficient for the learning algorithm to generate the results we want. What if we could import the initial neural network weights from other organizations? Would another site’s network be representative of our own network?[…]