Data centers play a crucial role in the collection and analysis of data for organizations across major industries. With the rise of data culture to speed up digital innovation, companies build data centers to facilitate data storage and processes. Companies have also started to develop business models that are data-driven to maximize the potential in utilizing data.
Impact of downtime on data centers
Zero downtime is essential for companies that rely heavily on data centers to handle their data processes. These outages cost organizations millions of dollars, which could be prevented with round the clock equipment and system monitoring.
In a study conducted by Ponemon Institute in 2015, the average cost of unexpected downtime on data servers is $9,000 per minute. The data was taken from benchmark research on sixty-three data centers in the United States and participated in sixteen industries. Financial services and e-commerce represents the largest in the benchmark samples, with 13% and 15%, respectively.
Fault tolerance and fault avoidance
Fault tolerance is the system’s ability to respond to software or hardware breakdown. There are different tiers on fault tolerance, with Tier I being the lowest. This level is able to keep the system functional even during a power outage.
Tier IV or level 4 data centers are said to be the most reliable data centers for companies that rely on two of the essential qualities in a server: availability and reliability. Level 4 centers run on two cooling and parallel power systems without a single point of failure. However, establishing Tier IV data centers are not too efficient in terms of the cost, and switching from Tier III to Tier IV does not offer much gain on availability as well.
Implementing Artificial Intelligence and Machine
At present, the majority of companies rely on skilled professionals to manage their data servers. These professionals are hired to bolster data security against hackers, develop algorithms to control workload distribution, or predict system failures.
With the advent of artificial intelligence and machine learning technology, data centers can find the use of its capabilities to operate more efficiently with minimal human intervention. There are many reasons why data centers should start to implement artificial intelligence and machine learning.
A data breach is a serious threat to data centers. As businesses cope with the surge of data, cybercriminals are becoming more and more aggressive in breaking in and stealing all the information in servers. In order to stop these occurrences, companies are obliged to hire cybersecurity professionals in order to protect the data stored by their organization.
Hiring IT professionals to monitor security breaches would also mean additional costs for the company. Artificial Intelligence can help data centers detect cybersecurity threats and malware by learning the processes and identify risk on signs of any deviation from the system’s normal behavior.
Data centers use up a large amount of electricity, with cooling systems as having the most significant share on its consumption. The U.S.-based data centers alone consume over 90 billion kilowatt-hours annually. Additionally, it is estimated that electrical consumption would surge as data traffic increases every year.
The high usage of electricity has been a problem for data centers. This is why companies are looking for solutions to conserve energy. Google, for example, has deployed AI to manage electricity use efficiently. The tech giant’s cooling system was able to reduce its electricity spend by 40%, as claimed by Google’s executives.
Downtime, as a result of a system outage, can disrupt business processes that could cost a significant amount of money. Companies hire professionals to maintain their data centers operational all the time. To reduce or avert downtime, the tasks of these IT professionals include decoding and analyzing several issues and identifying the root cause of every problem.
Artificial Intelligence can be a viable solution to system monitoring problems. Some of the tasks that AI can undertake monitoring network congestions, system performance, and congestions in the network to predict system failure.
Maximize server optimization
Datacenter engineers design algorithms to balance workloads in multiple servers and physical storage equipment to store and process data. However, with the increasing amount of data being generated, this approach may not be too efficient.
By implementing AI in the data centers, workloads can be distributed across servers through predictive analytics.
An outage due to equipment failure can be costly to businesses; hence, the need for constant monitoring of equipment. System failures can be a usual occurrence in data servers, which is why companies need data center engineers to need to detect flaws and do repairs regularly.
Artificial intelligence can take on the task of monitoring the equipment. It can use pattern-based learning to identify equipment defects to avert system failures through the use of smart sensors.