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Bots in Datacenter: Friend or Foe?

Bots in Datacenter: Friend or Foe?
May 16, 2018

It is always interesting to see how technology is shaping the future of business. And, one of the frequently discussed disruptive trends is the invasion of bots. The recent advances in AI technologies such as Natural Language processing (NLP) Deep Learning (DL) and Machine Learning (ML) have created intelligent bots that are ready to impact datacenter management and operations. However, more than the euphoria, there is a sense of skepticism that these advances in automation may lead to the replacement of humans. Let’s discuss it in this article.

Change Is The Only Constant

‘Bots’ is an umbrella term for intelligent automation infused with AI and ML. The underlying premise is intelligent bots teach themselves rules to mimic human insight and tactical thinking, without having to be explicitly programmed or scripted. These systems can learn to identify and classify input patterns, probabilistic prediction, and unsupervised operations.

Businesses today largely have two goals to accomplish. First, they are under relentless pressure to cut down costs, and it is the bots (intelligent automation) which facilitate cost cutting. Second, enterprises have to deliver better experiences to their customers and create more intelligent and intuitive interactions.

The days of placing annoying calls to human operators are gone and cognitive assistants such as Siri are giving way to interactive experiences. Here are a few enterprise scenarios where ‘bots’ have made a profound impact on infrastructure service providers, cost structure, and delivery capabilities.

  1. Preventive Bots: Recall a scene from Minority Report, a 2002 American science fiction film directed by Steven Spielberg where PreCrime, a specialized police department, apprehends criminals based on foreknowledge provided by psychics called precogs.

    Similarly, these bots are basically self-learning systems that provide predictive inputs based on historical trends. They predict and prevent issues beforehand by proactively detecting problems through smart correlation and clustering of alerts across technology ecosystem.

  2. Self-Service Bots Or Chatbots: When calling a customer care center at one of your banks or using an IM function on the help section of any company's website, we are invariably interacting with a bot agent that will run all service operations with minimal or no human intervention. On the phone, such interactions are very obvious (from the robotic voice, if nothing else) and we have grown accustomed to this sort of interaction.

    Similarly, in datacenter operations, cognitive assistants are now being leveraged for handling L-0 & L-1. AI powered virtual chat agents can significantly reduce Level-0 support workforce by empowering users for tasks such as password reset and network settings.

  3. Self-healing Bots: Simply put, these AI bots can detect and correct application infrastructure issues without human intervention. It’s quite intriguing to know how they actually perform in the data center operations. Let me give an example to explain this by taking a scenario in which every system admin/data center operator is familiar with the message ‘disk is running out of space.’
    • Alerting: Event that is triggered when true values differ from expected values. An alert is triggered when the amount of free space becomes less than 20%. The alert is triggered so that action can be taken to address the problem—preferably an automated action.
    • Logging: Monitoring of logs in real time, looking for predefined alert messages. Monitoring that addresses trending over time gives us a much better understanding of why the disk is getting full. The type of alert and where it originated plays an integral role in determining what type of action will be carried out.
    • Trending: Monitoring of values over an extended period of time. Trending also gives us an insight into how quickly the disk is filling up and how much time remains before that happens. And, trending over a period of time offers further awareness in the form of capacity analytics to data center operations team on impending issues. It calls for a remediation measure such as provisioning of additional storage.
  4. Analytics Bots: Data is growing exponentially and so is the business value that comes from gathering and analyzing Big Data. However, it's nearly impossible for humans to manually locate those chunks of data. And with advancements in Internet of Things (IoT) data prospecting challenges will add mining huge amounts of fast streaming, real-time machine -generated and operational transactional data to the mix.

    By introducing analytical-type processing into the data storage layer, we can aim at tackling huge scales of information available today and produce near real-time feedback to the business side of our organizations.

Bots have arrived in datacenter technology at many levels—applications, augmented operations, and management. They are even embedded in devices. Thanks to these bots that infrastructure and operations are getting smarter while gaining scale and speed. Now, the greatest concern is if this will result in data center automation or automation of other tasks, in which case the requirement for manpower will be greatly reduced. Some estimates reveal that 80% of current IT processes can be automated.

Thanks to these bots that infrastructure and operations are getting smarter while gaining scale and speed.

Does this put the IT personnel at risk of termination? IT professionals need to be updated with their current skills and look at reskilling efforts in areas such as cybersecurity, BI, and analytics. Since all this promises better, faster, and stronger data canter management and operations, invasion of AI bots is fine.