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Let us know which data center you'd like to visit and how to reach you, and one of team members will be in touch shortly.
The data center industry has become a core part not just of modern business but of modern life itself. As Artificial intelligence (AI) proliferates, it is likely that it will become key to the future of data center management. Here is a brief guide to what you need to know.
The end goal of all data center management is to ensure that the data center is operating at 100% efficiency 100% of the time. To achieve this goal, it has to ensure that all of its component parts are being efficiently deployed at all times.
This has two implications. Firstly, data center managers need to know what resources are needed in what place. Secondly, data center managers need to be confident that all of their resources are operating as intended all of the time. They cannot be compromised either by technical failures or by security breaches.
Before the adoption of AI-powered tools, data center management relied heavily on manual processes. These were not only very labor-intensive but also very prone to human error. The creation of AI-powered tools is enabling a more efficient and less stressful way of running data centers.
Artificial intelligence is beginning to play a role in many of the routine tasks involved in data center management. By doing so, it has freed up the time of human staff for value-added tasks. Here is a brief guide to the key technologies currently in use in data center management and the benefits they offer.
The ability to automate certain tasks is arguably the single, biggest selling point of artificial intelligence. In fact, it’s the benefit from which all other benefits are derived. For example, predictive analysis and machine learning are both based on the foundation of automation.
Automation is used in data centers in much the same way as it is used in other industries. That is to say, it is used for repetitive tasks, generally ones that require a high degree of precision. In the context of data centers, this can mean tasks such as energy management (including cooling and power management), inventory management, and update management.
Predictive analytics is what people often mean when they use the term “big data”. It’s the mining of vast quantities of data to extract its most important insights. These insights are then used to inform decisions regarding future actions. The concept of predictive analytics has existed for centuries. Artificial intelligence has, however, brought it to a whole new level.
The first benefit of using artificial intelligence for predictive analytics is that it makes it possible to analyze more data in less time. The second benefit is that it makes it possible to do so very conveniently. In many cases, researchers can simply provide the data and the relevant parameters and leave AI to do the rest. If necessary, humans can then double-check its results using the raw data.
In the context of data centers, probably the most obvious use for predictive analytics is in predictive maintenance. This has been crucial to improving the overall efficiency of data centers. It is, however, also used in many other areas such as security compliance and capacity planning.
Machine Learning (ML) is a sub-branch of predictive analytics. The key differentiator between machine learning and regular predictive analytics is that machine learning focuses on developing AI algorithms that continually develop without direct human intervention.
Essentially, machine learning aims to replicate the experience of human learning. Specifically, it aims to create algorithms that can identify key information from existing data and then apply what it has learned to new data without explicit instructions to do so.
This difference means that machine learning tends to be used when data center managers need decisions to be taken in real time (or very close to it). For example, in the context of data centers, machine learning tends to be used in areas related to resource allocation such as workload management.
The headline benefit of artificial intelligence in data center management is that it boosts efficiency and hence leads to cost savings. Additional benefits include improved reliability, security, and scalability.
At the same time, the use of AI brings its challenges and considerations too. Probably the first of these (literally and figuratively) is the upfront cost of investing in AI-powered tools. Additionally, these tools require staff who understand how to use them effectively. The use of artificial intelligence can also lead to questions about security and privacy, especially when AI-powered tools are used to store and process sensitive data.
Finally, AI can generate ethical questions. This is currently less of an issue in the context of data center management than in other areas. It may, however, become a more pressing concern in the future.
Related Resources:
Challenges And Ethical Considerations In AI-Enhanced Data Centers
Data disaster recovery in data centers refers to an organization’s plan to recover its data in the event of a disaster. In IT, a disaster is simply an unpredicted event with negative repercussions. This can mean anything from unscheduled maintenance causing network outages to the impact of civil unrest.
At a micro level, the potential risks in data center network security are, however, continually evolving. For example, DoS attacks became DDoS attacks. DDoS attacks then transformed from attacks run directly by humans to botnets launching automated attacks that leveraged known vulnerabilities in server protocols.
Data centers exist to store and process data. Modern data centers are storing and processing larger volumes of data than ever before (big data). In some cases, they’re also required to process it faster than ever before. A data center’s choice of data storage is therefore key to its performance.
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