Network analytics is a key element of intent-based networking. Understanding how it works is not that difficult.
Our customers are telling us that their IP networks are more strategic to their core business than ever, and this means that the network services, applications, and security need to be aligned with the business goals. Key to achieving this is network assurance. This new category of networking solutions is where procedures are implemented that align the operation of the IP network to the intent of the business. This is achieved with computer analytics which are used to find anomalies in network, services, and applications that are detracting from the network intent. Analytics is where a computer compares incoming data against pre-programed operational models and makes functional decisions, in order to improve the operational process.
Analytics has been used for years in process control networks for manufacturing, utilities, mining, and oil & gas. Its success is undisputed in being able to make these industrial networks safer, more productive, and less expensive to operate. Analytics in process control began life many years ago as simple sensors connecting to a controller using if-then commands like: “if the pressure goes above X, shut the oil pump down.” Or: “If the motor rises above a certain temperature, slow to half speed.” With time, these commands became more complex: “If the temperature is above N, AND humidity is above X, AND vibration sensor reaches level Y, do Z.” Expanding this process out results in the building of a “Model for Analytic Computation.” A typical analytics model will have a long string of these “if-then” strings that allows decisions to be automated based on this pre-programmed model. The model is usually updated from time-to-time as the network conditions change.
Today’s analytics engines are augmented by machine learning. This is where the analytics model is updated constantly, and in real time, based on changing conditions in the network. In a future blog, I’ll discuss how machine learning works and how it can improve the analytics model. However, it is important to note that analytics systems have operated for many years, and with incredible levels of success, without the use of machine learning.
Let’s circle back to Cisco and our solutions for IP networking assurance. We can draw many parallels between Cisco’s assurance solutions and these analytical systems for industrial process control. Cisco’s assurance solutions, which can be found in a myriad of our products, such as DNA Center, Meraki Insight, and Network Assurance Engine (NAE), rely on analytic models for IP networking. Taking DNA Center as an example, the assurance feature within DNA Center, consists of a complex analytical model which is based on 30 years of Cisco networking experience. DNA Center pulls data in the form of streaming telemetry from network endpoints as well as switches, routers, and servers. This data is fed into the analytical engine where it is correlated against thousands of variables. Then, metadata from this information is extracted and aligned with the context in which it was received. Context is the who, what, when, where, and why that can cause two seemingly identical events to result in very different decision outcomes. DNA Center then provides actionable insights based on the analytical engine’s outcomes. When changes exist that could optimize the network, DNA Center provides suggested remediation with specific actions and processes. Following the execution of these changes, DNA Center’s assurance engine verifies that these modifications resulted in the optimal configuration in order to align with network intent. This process not only optimizes the network for increased employee productivity, it also greatly decreases the amount of time that IT spends on troubleshooting tasks. This frees them up so that they can focus on projects that increase your core business, such as migrating applications to the cloud, improved CRM systems, and increased network security. In this way, networking assurance not only optimizes the productivity of your IT staff, it optimizes your network to better support your core business.