The modern data center is an incredibly complex system. Between managing equipment, maintaining networks, regulating energy and environmental controls, and providing both physical and cyber security, the data center faces a wide array of challenges in a typical day.
Keeping that complex system up and running is difficult enough without accounting for potential problems that might arise in the future. Fortunately, the last several years have seen significant improvements in predictive analytics, which can be a powerful tool for data centers seeking to provide consistently superior service.
Simply put, predictive analytics utilizes statistical algorithms and AI-driven machine learning techniques to analyze data gathered over time to anticipate future outcomes. While the concept has been around for some time, recent technological advancements have made predictive analytics more cost effective and practical. Many industries now turn to this tool to improve efficiencies, minimize waste, and reduce costs.
Here are a few ways predictive analytics can benefit a data center:
Data centers are undergoing constant changes because the needs of their customers are constantly shifting. Companies frequently spin their computing and data storage requirements up or down based on demand, while new customers place new pressures upon existing server arrangements. Server racks need to be added or moved on short notice, especially for colocation centers trying to accommodate new equipment.
In a data center, however, there’s no such thing as a simple change. Every adjustment has knockdown effects throughout the facility’s infrastructure. Boosting deployment density affects power requirements, which in turn changes cooling demands, and so on. In such a complex system, it can sometimes be difficult to anticipate the potential consequences of any change. What would happen, for instance, if a server were moved from one rack to another?
Simulation programs driven by predictive analytics can solve this problem by running through, model-based simulations that take into account a variety of complex variables. They can anticipate how moving equipment will affect heat dispersion or network latency. Armed with this information, data center technicians can manage equipment and services with minimal disruption. By flagging potential issues, predictive analytics simulations can also help to avoid major problems that would otherwise require significant time and effort to troubleshoot and resolve.
Disasters that lead to data loss are the nightmare scenarios that keep data center engineers up at night. Unexpected problems such as faulty backup systems, power overloads, and cooling failures can not only lead to significant downtime, but inflict physical damage to equipment that results in data loss. While natural disaster like hurricanes are certainly a concern, seemingly minor equipment failures can lead to catastrophic consequences if they’re not addressed promptly.
Predictive analytics can model a wide range of disaster scenarios, which helps data center personnel to formulate plans of action to resolve the problems. If cooling systems were to fail, for example, simulations can determine how much time can elapse before rising temperatures force other systems to shut down. While good data centers perform regular tests and conduct drills to test disaster readiness, there are some scenarios that are difficult to play out. After all, deliberately overloading a server to see how it affects the rest of the units in the rack would be an expensive and risky exercise. With predictive analytics, however, data centers can find out what they can expect to happen should such a scenario occur.
Managing power is one of the most critical tasks carried out continuously in any data center. The slightest fluctuations can have tremendous consequences, affecting temperature levels and server performance. Power requirements are anything but static, especially for customers who need a lot of processing power to carry out their business operations.
Fortunately, today’s data centers are equipped with sensors and systems that carefully monitor power usage down to the server level. They can determine when power usage increases and what periods of time utilize less power. Armed with this data, predictive analytics can model power usage trends and anticipate future demands, which can improve reliability and lead to significant cost savings.
Delivering reliable services is the primary goal of any data center. If customers can’t count on a facility to provide reliable uptime, they’re going to start looking elsewhere for their data needs. By deploying predictive analytics to predict network usage trends, data centers can structure their deployments and manage resources to ensure that customers always have access to their data and applications.
But usage patterns are only one aspect of prediction. Data centers rely on their equipment functioning at an optimum level to provide consistent services. Predictive analytics can estimate the lifespan of much of that equipment based on performance patterns. If a simulation indicates that a server is likely to fail within the next six months, the data center can make plans to replace it before it actually fails, which is not only more cost effective, but also ensures that there will be minimal disruption to services.
By harnessing the power of predictive analytics, data centers can better prepare for problems they may encounter in the future. Improved preparation helps to reduce costs, which allows facilities to invest in other areas to serve customers more effectively. Most importantly, predictive analytics can deliver measurable benefits in the form of better uptime, allowing data center customers to focus on growing their business with the reassurance that their data will continue to be secure and accessible.