The network revolution is happening right now! In this digital era, organizations are trying to make the right decisions on their digital initiatives to stay ahead in the game. Hence, it is not surprising to see all the remarkable advancements that the year 2020 holds around technologies such as 5G, SD-WAN, Edge computing, Wifi-6, etc. But what we need to realize is any technology is only as good as the way it’s managed, period.
While today’s enterprises value service assurance and network uptime as their top objectives, they are inclusive enough to adopt network transformation to achieve these objectives. That is why they are also the ones intelligent enough to see the next wave coming - the wave of Artificial Intelligence.
A lot of interest has been around a term coined by Gartner called AIOps, which is set to be the network genie for years to come. As self-explanatory it can be, it is NetOps powered by Artificial Intelligence. It is garnering a strong and renewed interest in restructuring the way we manage our network infrastructure. But what defines AIOps, and what value does it offer the modern enterprises? Let us scrutinize it with some human intelligence.
#1: Meet AIOps - Your new teammate
Whether AIOps is a reality, only time and investments will cement. But right now, the excitement around it is exploding across many networks and enterprises. It is not hard to guess why. AIOps intends to raise the bar of NetOps. It aims to drive the enterprises to innovate on the business side and forget the hassles that networks have traditionally offered them. By whipping in intelligence with network automation, this new leap is going to accelerate the velocity, response-time, and application-relevance and on-the-spot readiness of a network.
Consequently, AIOps is set to optimize and reduce the costs that networks incur. That also means that there is a flurry of activity around it in the market already. Is any offering worth living up to its quintessential promise?
Many vendor platforms have started offering AIOps as a solution. However, the lack of a strong foundation around network automation and network monitoring is going to leave a hard-to-miss void in any solution. Here are a few key aspects of AIOps that will play out significantly for a successful vendor and, thus, bolster a successful business.
#2: Embrace data at scale
AI is intelligence, and intelligence thrives on data. Deemed as the most critical element in AIOps, data in any form from the network is going to be the pivotal feature here. Enterprises need to break the organizational silos and try to create that one database of real-time information that straddles across multi-vendor, multi-domain, and multiple sources across the legacy and hybrid multi-cloud infrastructure. This diversity is critical in getting an accurate and real-time view of all the machinery in a hybrid environment.
It is vital to get a grip on it as early as possible. As with a plethora of applications and technologies around, the amount of data that the network generates is set to explode. This surge, incidentally, can play out as a blessing in disguise for AIOps as it is primarily driven by the volume of data it ingests. On-demand big data scale, speed steered by cavernous queries for exploration - all these are going to build the foundation of future AIOps.
Dilip
Krishna S,
Product Marketing
Manager,
Anuta
#3: Automated Baselining with Machine Learning
Dynamic is not an adjective for AIOps; it is its core value. A huge shift from a rule-driven approach to dynamic baselining through Machine Learning (ML) will take NetOps to the next level. It is important to train these ML systems on expected behavior paths at a very granular level. This sense of calibrating and re-calibrating the system to learn granular details of network events helps ML systems to strongly mature over a while. It is way beyond a one-time exercise. ML also plays a crucial role in alert-grouping to reduce the signal-to-noise ratio and alert-correlation through pattern-matching. This strength also detects anomalies from expected behaviors, builds dynamic thresholds, and predicts outages and performance issues. But remember, all said and done, any ML system is only as good as the data it chews.
From the various discussions we have had, ML seems to be an area of uncertainty for many. But we are certainly going to gain more confidence in this area and see more use-cases that benefit businesses.
That is why AIOps holds such a bright future ahead.
#4: Dissect the unknowns with Predictive Analytics
Who does not dream of a proactive network – One which can give you an early warning of potential issues even before they start to shape up? Identifying security threats is one such example. Predictive analytics, powered by NetFlow, can detect a lot of patterns to proactively indicate the devices and applications that can be at risk.
However, there are two aspects to achieving this utopia of proactive networks. One is the alerting realm where the intelligence gathered through the data and ML systems triggers predictive alarms. The continuous flow of time-series data measured against a dynamic threshold set by ML relays the information about an impending issue in your network. This forecasting relieves NetOps from the initial triage, helping enterprises to maintain the network uptime, lower mean time to repair (MTTR), and meet SLAs. As predictive analytics matures as a technology, we see prescriptive analytics making waves too - offering you the best course of action for any issue in the network.
The second aspect is about visualization. It is all about presenting the information in an easy-to-use format. Visualization offers actionable insights to the NetOps teams helping them to pinpoint issues and discuss corrective actions. Without effective and easy visualization, all the leg work done by data, analytics, and intelligence is a wasted effort.
#5: Network Uptime - All the time
Automated service assurance is key to any organization's customer success. AIOps will aid NetOps by automating manual tasks and accelerating innovation. AIOps powered by workflow automation, service modeling, compliance capabilities, and closed-loop automation - can empower organizations to redefine network automation with a slice of difference through Artificial Intelligence. With one hand on ML and Analytics, the measurement of feedback at every stage of automation is more accurate and contributes to further learning. AIOps will pave the way for the much talked about Intent-based networking too.
As you can comprehend, AIOps is much more than just another acronym. It packs a new level of actionable intelligence, immunity, and business advantage for enterprises that want their networks to stay ahead of every curve.
It is new, but it is gaining steam. Adoption of AIOps may be a slow process, but we are expecting some significant advances in the coming year. This shift will need a cultural change, both at an organizational level and an individual mindset. Today’s networks are complex, and it is difficult for humans to catch up. There is a lot of room and need for technologies such as AI to step up, provided you identify the right kind of use-cases for it.
At the same time, we cannot forget that any digital transformation exercise is a journey, so is network and operational transformation. Get ready to change the way you build, operate, and manage networks with AIOps.
Welcome your new comrade in NetOps!
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