Data centers today process ever larger, more complex volumes of data. Hybrid cloud, 5G wireless, the internet of things (IoT), and artificial intelligence (AI)-based applications are pushing traditional data center capabilities to their limits. Data centers require agility and intelligence to effectively manage these increasing demands and workloads.
Forward-looking data center and colocation providers today leverage AI to respond to these challenges. By doing so, they reduce costs and energy usage, while improving performance and providing superior service. Here are some of the major data center issues AI can address.
Data centers consume a lot of energy. And since the amount of data they need to process is growing exponentially, data center hardware will have to work harder, requiring even more energy to power and cool the servers. This raises costs and increases the data center’s overall resource footprint.
Fortunately, AI can help prevent overheating while simultaneously saving power. AI-powered smart sensors can monitor and analyze data from electrical, mechanical, and environmental sources, detecting energy inefficiencies and informing real-time adjustments in critical systems. Historical data can be used to predict future pressures and temperatures in the data center, allowing data center operators to make informed plans that reduce energy consumption and cut costs. Google, for example, reduced its data center cooling bill by 40% thanks to AI.
Data centers have long been a target of cyber-attacks. Identifying constantly evolving threats while attempting to harden and fortify your systems against them is a time-consuming, resource-intensive, and never-ending task. New threats continuously emerge, and human ingenuity alone can’t keep up.
AI cybersecurity systems can monitor and analyze all network traffic in real time. By doing so, they can detect anomalies and aberrations in massive data sets, uncovering malware, spam, zero-day exploits, and other forms of cyber-threats. These systems can also run sophisticated simulations to discover potential weaknesses and reveal security issues buried deep within data sets. Finally, AI cybersecurity algorithms can also detect subtle variations in user access over time, revealing potential insider threats and vulnerabilities exposed through human error.
Unplanned downtime in data centers can occur for many reasons: systems failures; equipment malfunction; power outages; inclement weather; or, again, good old human error. Data center operators need to identify root causes as quickly as possible in order to get the data center up and running again. The longer it takes, the greater the potential loss of money, data, and resources.
AI monitors can track and analyze systems performance, power levels, workload distribution, network congestion, and disk utilization. Predictive analytics can be used to optimally distribute workloads, reducing strain on servers and maintaining desired levels of efficiency across the data center. AI-based deep learning (DL) applications can even be used to predict and detect outages before they occur. What’s more, they can perform these analyses far more consistently and accurately than humans traditionally could.
The ability of AI to monitor systems and act upon operational issues allows data centers to optimize staff allocation. Automating routine tasks, such as resource and service provisioning or help desk support, greatly reduces the need for staff to provide basic tech support. This means existing staff can focus on providing customers and partners with higher levels of service, as needed. As AI takes over an increasing number of automatable functions, data center employees can move into facilities management roles, a critical need as data centers proliferate, growing larger and more complex.
Predicting when equipment might fail isn’t as easy as it sounds. Manufacturers do specify what the average lifespan for a piece of hardware is, and that figure is usually pretty accurate. However, there are a lot of variables to contend with, such as heavy usage, accidents, unknown defects, or improper usage. These factors can easily reduce the lifespan of a piece of equipment; having to replace it unexpectedly could result in costly unplanned downtime and frustrated customers, or worse.
AI-based predictive analytics can monitor equipment usage, tracking real-time performance and historical patterns to accurately calculate when hardware is in danger of failure. Early warnings about potential failure allow staff to run necessary quality assurance tests and fix any underlying issues or, if necessary, replace the piece of equipment with minimal service interruption.
While different AI technologies must be implemented by different stakeholders – tenants managing their own equipment, service providers at the network level, or the facility operator – it is crucial to choose data centers and colocation service providers that proactively incorporate AI into their operations to ensure dependable service and optimal performance. At Netrality, we pride ourselves on our commitment to utilizing the latest technologies to provide the highest levels of service and efficiency. For more information about connecting to the edge at Netrality’s interconnected data centers, contact us.