The role of advanced analytics in networking – ITProPortal

The networking space has evolved dramatically over the last two years as organizations realize the increasing value of AIOps, the benefits of full network visibility, and the role secure access plays with remote workforces. Network analytics and monitoring procedures that once were considered standard have quickly becoming inadequate in today’s rapidly changing IT network landscape. But fortunately, advanced networking analytics leveraging Artificial Intelligence (AI) and Machine Learning (ML) are helping to overcome new challenges when it comes to maintaining network performance and future-proofing NetOps teams.

As a quick refresher, network analytics applies data analytic techniques to network data to monitor complete network behavior. With the addition of AI/ML technologies (and the rise of AIOps), deeper insights into application and network performance can be drawn on network data. These insights help NetOps teams troubleshoot networks and make goal-based improvements more efficiently while helping organizations effectively make intelligent business decisions. For example, these might include extracting insights and drawing patterns around anomaly detection, forecasting, outlier detection, predictive analytics, utilization and application baselining, and more.

As AIOps continues to evolve, many are polarized by the idea that networks are far too complex for fully closed-loop automation. But automating general tasks is considered routine and trusted when the impacts are thought to be minimal. As technology matures, advanced analytics are helping to redefine the role of automation. To help outline the layers involved in automating IT networking operations, it’s helpful to understand the five layers within AIOps that represent the sophisticated algorithms and advanced analytics being used. Each performs an advancing step towards automated management of networks. Let’s review these now:

1. The initial data set or source – It all starts with data (big data), which often includes the massive influx of seemingly unrelated data – much of it redundant and/or corrupt. Data set selection algorithms are used to clean up much of the noise. However, implementing these algorithms requires complex and specialized mathematics, making it challenging for vendors that underestimate (or don’t recognize) the significance of the noise.

2. Pattern recognition and data analytics – Popular pattern discovery algorithms are then applied, for example, template discovery, which is an approach that highlights patterns already present in the data, rather than discovering deeper insights. Deeper insights use deterministic or statistical approaches to go beyond the content and expand what is known about the data and deliver actionable insights.

3. Inference algorithms – These algorithms adapt to the data at hand and go beyond pattern discovery. More statistics and logic can be applied to draw out deeper insights. At this stage applying inference, such as “what-if” experiments, can establish causal links between different events.

4. Communication and collaboration – After deep insights are found, they’re translated into many forms to be visualized, expressed in natural language, or machine-readable language. The idea is to formulate findings and propagate insightful action.

5. Automation – Automation is the capstone of the AIOps workflow. But it’s also the trickiest due to complex networks that may not easily be modified while operating. Few examples of automation include (a) pushing an Access Control List on the firewall to block traffic originating from a specific IP address (b) configuring a routing policy or changing a route metric to switch traffic between two uplinks, and (c) pushing policies via controller (for e.g., SDWAN) APIs to meet application performance requirements.

What are the operational benefits of applying this model for networking? By implementing a single unified network monitoring solution that utilizes AI/ML advanced network analytics and automation, organizations gain benefits that contribute to reduced costs and greater operational standards. Specifically, this can be valuable in three primary areas: advanced reporting for baseline and trend analytics, real-world security investigations with network analytics, and root cause analysis with packet data. 

First, baselining is a common strategy to stave off downtime and capacity problems that lead to performance issues. Network monitoring solutions can easily report on baselines as well as trends. Some even use AI and ML to predict when to add capacity and alert teams when the network or network segment is deviating from the typical baseline, even responding to incidents automatically. Typically, as the size of the network increases, it’s important for operators to focus on issues that deviate from historic baselines rather than absolute values. Application performance KPIs such as loss, jitter, and latency vary based on a variety of factors across the entire network. However, it’s likely that they all follow their respective historic patterns. So, any deviation from these baselines is a very powerful insight and further helps reduce noise.

In-depth analytics

Second, as the internet’s capacity increases so do cybersecurity threat vectors. Recognizing and stopping breaches as they occur is priority number one, but if attacks should go undiscovered until it is too late, the weakness may never be corrected. Fortunately, advanced network analytics and forensics – the recording, storage, and analysis of traffic – gives IT organizations and security experts the comprehensive data they need for finding proof of attacks. Retrospective analysis especially in the SecOps space relies on the ability to go back in time and identify patterns associated with threats and learn from it and be able to recognize similar patterns when they happen again in the future. This is one of the key areas where AI/ML techniques is making a huge impact in pushing the needle forward.

And finally, while incidents may impact the performance of the network, discovering the root cause requires investigating deeper into the details to resolve the problem. Network monitoring solutions can access in-depth analytics on network activity such as bandwidth utilization, application response times, flow volume, packet types, expert events, security events, and VoIP calls.

In addition to these under-the-hood aspects, daily NetOps benefits tremendously from the advanced network data visualizations, allowing teams to understand at-a-glance the health of the public and private network traffic, and with interactive capabilities to drill down into the details of flows and devices. Two important visualizations include flow visualizations and QoS monitoring. Flow visualizations integrate flow and packet-level data to give real-time and historic top-level views. QoS can be monitored on a per-class basis, easily visualizing the impact QoS policy changes have on network and application performance. 

For those still unsure of the role that advanced analytics plays with NetOps teams, consider how quickly these technologies have transformed other industries such as the financial services and healthcare sectors. It’s a safe bet that advanced network analytics and AIOps will play a major role in streamlining security and network performance over the coming years.

Vishwas Puttasubbappa, SVP of R&D, LiveAction

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