Very often an Internet of Things (IoT) environment behaves normally, it does what its designers intended it to do. However, from time to time things go wrong, says Will Cappelli, CTO EMEA and Global VP of Product Strategy at Moogsoft, events can take place which indicate that the platform is not behaving in a way which was anticipated or desired. Therefore, an intervention is required to reset the course and readjust things. But how does this process work?
The traditional approaches to IoT incidents
Generally, it can be described as a link between a signal and a response – the environment signals that something is wrong which is sent to a responder. The responder, which could be human or robot, acts on the signal and changes things. If you look at traditional IoT environments there were two types of signal/response mechanism.
Firstly, there was the “fast but dumb” response mechanism which most IoT environments use. You would have a signal that would travel along a hardwired path to a specific responder which would usually only do one thing, or it might select from a menu of three of four things and look to fix the sensor or restart the network. It would work very quickly.
The second approach is “smart but slow” that has mainly been available via log management vendors. This approach is based on trying to make the right decision in a complex environment – you can’t just have a couple of options to choose from. You need to respond to each situation uniquely.
The theory is to accumulate vast amounts of data about the environment in an unstructured log management database and supply decision-makers with a whole bunch of tools to make sense of the environment, providing them with a broad pallet of choices as to what might be the best solution.
Unarguably, this can produce very accurate results but the process is slow, the latency can last for weeks. Particularly in an IoT setting, that makes no real sense as you don’t have that kind of time to make the right decisions.
Why use AIOps in IoT
Both these scenarios are before the emergence of Artificial Intelligence for IT Operations (AIOps). What AI and AIOps approaches create is that via automation the analysis of the task can give you a “fast but smart” way of managing an IoT system. It effectively takes the slow but smart model but automates the human insight, resulting in a significant reduction in latency between signal and response – but crucially not sacrificing the quality of the response. At a fundamental level, that is what AIOps brings to the table in terms of managing an IoT environment.
Let’s be more specific. By examining behavioural trends and predicting where these trends are impacting the network, AIOps can rapidly predict incidents before they occur. AIOps also significantly decreases the amount of time it takes to figure out what the root cause of a performance problem is. Essentially, it can help you look into the future and look into the past. In addition, [...]
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Anasia D'mello, Khareem Sudlow
