The fourth Industrial Revolution brought about a data culture that accelerated digital transformation. The organization is developing data-driven business models to maximize the use of data. As a result, data has become an asset and an important part of almost every business operation. Indeed, each organization has begun to implement active data collection and analysis for various applications. To this end, organizations deploy large data centers to store and process data.
In addition to these data centers, organizations need to engage skilled professionals to maintain and monitor the data centers. For each organization, the cost of running data centers and hiring employees is very high. In addition, monitoring and surveillance personnel is an additional task. Therefore, organizations have been looking for better alternatives to traditional methods. As an alternative, organizations can deploy AI in data centers to handle various tasks autonomously, such as server optimization and device monitoring.
For each data-driven business, the AI. in the data center must be utilized in a feasible way Gartner claims that more than 30% of undeployed AI and machine learning data centers will not be operational and economically viable by 2020. Hence, each data-driven business must implement AI and machine learning for its data centers. AI will also help organizations stay ahead of growing data storage and processing requirements.
Implementing AI in the data center
Improving security
Data centers are vulnerable to different types of network threats. Internet criminals have been looking for new ways to get data from data centers. To this end, hackers will regularly develop more advanced malware strains and plan for cyberattacks that may secretly infiltrate the organization’s network. With such malware, hackers can access confidential data from millions of users. For example, a security researcher recently reported a massive data leak that exposed 773 million emails and 21 million passwords. Data leakage can be very dangerous because it has collected data from various sources, resulting in a unique combination of email addresses and passwords of 1.6 billion. Such data leaks are common for data-driven enterprises. Therefore, each organization employs cybersecurity professionals to analyze new cyberattacks and develop prevention and mitigation strategies. However, for network security experts, it is very labor-intensive to find and analyze network attacks.
organizations can deploy AI in data centers to ensure data security. for this purpose, AI can learn normal network behavior and detect network threats based on deviations from that behavior. Furthermore, using AI in a data center can detect malware and identify security vulnerabilities in a data center system. Furthermore, AI-based network security can thoroughly filter and analyze incoming and outgoing data to view security threats.
Conserving energy
Running a data center consumes a lot of electricity. A large part of the energy is used for cooling systems in data centers. In the United States alone, data centers consume more than 90 billion kilowatt-hours a year. Globally, the data center consumes about 416 terawatts of electricity. Therefore, energy consumption is an important problem in data centers. In addition, electricity consumption will double every four years as global data flows increase. To save energy, the organization has been looking for new solutions.
Technology giants are using AI in data centers to save energy. For example, Google has deployed artificial intelligence to make effective use of the energy of its data center. As a result, Google executives reduced their data center cooling system by 40%. For industry giants like Google, even saving 40% of energy can be equivalent to millions of dollars worth of energy savings. Again, every data-driven enterprise can deploy AI in a data center to save energy. AI can learn and analyze temperature set points, test flow rates and evaluate cooling equipment. Companies can also train their AI. by collecting key data with the help of smart sensors By this approach, AI can identify sources of energy inefficiency and automatically repair these inefficiencies to reduce energy consumption.
Reducing downtime
Data interruption in the data center may result in a large number of downtime. Therefore, the organization employs skilled professionals to monitor and predict data interruptions. However, manually predicting data interrupts can be a complex task. The staff of the data center must decode and analyze multiple problems to find out the root causes of the different problems. However, the implementation of AI at the data center could be a viable solution to the crisis. AI can monitor server performance, network congestion, and disk utilization to detect and predict data interrupts. with the help of AI, organizations can use advanced predictive analytics to track power levels and identify potential defect areas in the system. For example, artificial intelligence-based prediction engines can be deployed in an organization to predict and identify data interrupts in a data center, and built-in signatures can identify users that may be affected. then, the AI system can implement a mitigation strategy autonomously to help the data center recover from the data interrupt.
Implementing server optimization
Each data center consists of multiple physical servers and storage devices used to process and store data. In order to process large amounts of data, engineers in the data center must design algorithms to balance the server workload. this approach has been shown to be inefficient in optimizing server performance due to the increasing rate of data generation and collection.
Deploying AI in a data center can help distribute workloads between servers through predictive analysis. load balancing algorithms driven by AI can be learned from past data to efficiently allocate workloads. AI server optimization can help identify possible defects in data centers, reduce processing time and address risk factors faster than traditional methods. In this way, organizations can maximize server optimization and performance.
Monitoring equipment
The data center engineer needs to monitor the equipment to regularly detect defects and maintenance requirements. However, it is always possible for the data center engineer to miss some defects in the system, resulting in equipment failure. For organizations, such equipment failures can be costly as they may require maintenance or sometimes replacement. In addition, equipment failure may lead to downtime, which can lead to reduced productivity and poor quality of service provided to customers. Device failures are common for data centers because they process more and more data every day. Such high processing requirements can heat the entire system, continuously affecting data center equipment. If the cooling system has undetected defects and stops working, the whole system will overheat and shut down. Therefore, equipment monitoring is critical for the organization.
organizations can use AI in data centers to perform active device monitoring tasks. AI can use pattern-based learning to identify defects in data center devices. For this purpose, AI can use smart sensors installed in the device. If the AI system finds any vibration too high or too low and unwanted sound, the system informs the data center engineer of possible defects. By this method, the potential equipment failure can be predicted AI implementation in the data center, thus avoiding downtime.
Changing staffing in data centers
Datacenter adoption AI will automate various routine tasks, such as temperature management, equipment condition monitoring, floor safety, fire hazard mitigation, ventilation, and cooling system management. Therefore, there will be no need for data center personnel to perform a large number of tasks. In this way, organizations can reduce the shortage of personnel. Furthermore, as the organization introduces AI, work such as basic technical support and management will become obsolete.
AI in the data center
Because the AI in the data center will automatically perform many tasks, the data center personnel can perform other tasks. Therefore, organizations will need to plan new roles for data center employees. In addition, organizations must introduce training programs for their data center staff. Using this approach, organizations can improve the skills of their existing employees and promote their professional development.
the emergence of AI in data centers shows the potential of various industries. Soon, AI will dominate the world of data centers and hosting service providers by proactively helping disaster recovery and compliance. Data centers and hosting service providers must, therefore, use AI to keep up with emerging technology trends and gain a competitive advantage.
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