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Data storage can be a pain to properly configure, but artificial intelligence (AI) is proving to be useful for helping do just this. The scale and complexity of workloads is increasing and becoming more difficult, and now teams of specialists are needed to manage the massive storage of data. However, AI techniques are allowing fewer storage engineers to efficiently manage the storage of large amounts of data.
AI can recognize visual cues faster than humans can and can interpret patterns in human language. AI has also been able to understand the concept of resource retention.
For example, Plume Wi-Fi uses a version of AI as their cloud optimizer called ARIMA, which is an AI technique perfect for the analysis of time-series data. It uses statistical models to analyze and forecast time-series data and explicitly cater to a suite of standard structures in this data. Each night, Plume’s AI takes in data from its customers and predicts what data usage patterns are likely to emerge at each site. Enterprise storage engineers also work alongside the AI to analyze the code the applications in the system must service.
Understanding these patterns is critical to understanding how their storage should be designed around their needs. Because of its ability to take in and analyze large volumes of data, AI gives engineers the possibility of discovering and minimizing problems more efficiently and effectively than any number of humans.
However, no single AI technique will solve every problem with analyzing and storing large volumes of data. The ARIMA model Plume uses would not be as efficient for a bigger company or enterprise. Other techniques available include convolutional and recurrent neural networks, which discover patterns in storage availability.
Though companies and enterprises are in the beginning stages of AI integration, it is a helpful tool in optimizing storage performance. AI can also spot performance problems and other anomalies, which can be relayed to human operators and suggest better ways to handle the issue at hand.
The adoption of AI can also lead to a decrease in IT payroll because fewer skilled engineers would be required to handle the same – or larger – amounts of data to be stored. It could also cause an increase in performance since a similar number of skilled engineers could be capable of devoting their time to the analysis and management of the large volume of data.
