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How ML And AI Tools Are Being Used In The Data Center

As data becomes a driving force in the business and IT world, data center artificial intelligence and machine learning tools can be the answer to leveraging data to solve business outcomes.

Machine learning (ML) and artificial intelligence (AI) solutions are helping organizations today stay ahead of the ever-increasing amount of data storage needed and processing requirements. New innovative AI and ML tools are being used in data centers across the globe to improve operational efficiency and productivity as well as solve some of the industry’s most glaring problems, such as data security and a data center’s carbon footprint.

CRN breaks down eight ways AI and ML tools are being used in data centers to solve some of the biggest technical, cybersecurity and data-related issues in the IT world today.

AI Vastly Improving Data Security, Data Outages

With data breaches and cyber threats like ransomware on the rise, AI can help prevent massive data breaches and hacks by learning a network’s normal behavior then detecting threats based on anomalies and deviations from that behavior. By leveraging AI tools in the data centers, businesses and IT teams can detect malware and identify security loopholes in data center products and systems. Many AI cybersecurity tools can deeply and effectively screen and analyze all types of incoming and outgoing data for security threats.

AI-based cybersecurity tools can process and analyze nearly all incoming and outgoing types of data, while also detecting threats such as malware. AI security solutions use behavioral analytics to protect all forms of data in the data center.

Additionally, AI predictive engine tools can be deployed to predict and identify data outages in the data center, with built-in signatures that can recognize users who might be affected. AI systems can automatically implement mitigation strategies to help the data center recover from a data outage.

Automate Workload Movements And Management

A slew of new AI tools are being launched to automate the movement of workloads to the most efficient IT infrastructure, whether it be in a data center, at the edge or in a hybrid cloud environment.

AI is striving to transform workload management to reduce time-consuming and manual tasks by data center operators, boost workload efficiency and cut down costs of having workloads in inefficient IT environments such as public clouds versus on-premise. Artificial intelligence helps allocate workloads in a more effective manner than traditional automation solutions by enable customers to become more flexible and scale faster.

Startups are sprouting up around AI for workload management such as DLabs, Redwood Software and Tidal Software. For example, Tidal Software is becoming a leading provider of enterprise workload AI solutions that orchestrate the execution of complex workflows across systems, applications and data enter environment. Tidal’s entire product portfolio revolves around leveraging AI to orchestrate complex IT workflows, optimize workload automation activities, and provide centralized management automation for all workloads.

AI And ML To Prevent Data Center System Failures

Data center system failures can be expensive for businesses as valuable time is lost repairing or replacing products. Additionally, a failure can be devastating to a company’s customers around network outages and slower services being provided.

Leveraging AI and machine learning in the data center can help predict potential equipment failures, thus avoiding costly downtimes. Business across the world are leveraging AI in data centers for active and real-time equipment monitoring tasks. Artificial intelligence can identify defects in the data center equipment using pattern-based learning and can autonomously implement mitigation strategies to help the data center recover from a failure. AI can use sensors installed in the equipment to find any issues which can immediately notify data center teams about possible defects.

For example, AIMS AIOps Platform applies automaton and machine learning to proactively monitor and predict anomalies before they occur; prevent bottlenecks and downtime; as well as automatically detects changes to resource consumption to help control cloud costs. AIMS says it uses machine learning to automatically set dynamic thresholds and generate alerts that adapt to a company’s “rhythm of business.”

AI For Data Center Cooling, Reducing Carbon Footprint

AI not only solves various IT problems, but is helping data centers’ operational efficiency. Data centers that leverage AI for more efficient cooling see a reduce in the amount of energy they spend on cooling which cuts down not only on energy bills, but on a facility’s carbon footprints.

Cooling a data center can consume upwards of one-third of the overall power of a data center IT stack. Operators are successfully implementing AI into their operations with new thermal cooling solutions.

For example, Siemens White Space Cooling Optimization (WSCO) uses a network of sensors to collect temperature and air supply data. Its AI engine applies the data to algorithms and calculates the required adjustments in airflow to maintain the correct temperature for each aisle of racks. WSCO goes beyond alerting human operators to temperature fluctuations and automatically makes adjustments itself.

By automating the control of cooling fans, for example, it reduces the risk of a thermal outage and maintains consistent air temperatures among server racks and white space. WSCO also reduces wasted energy by matching cooling to the IT load in real time, automatically responding to temperature changes and eliminating overcooling.

Machine Learning For Server Optimization

In order to achieve maximum server performance and optimization in the data center, companies are implementing an array of new machine learning tools. ML-based server optimization solutions can help find possible flaws in data centers, reduce processing times, and resolve risk factors quicker than ever before.

Top notch machine learning and artificial intelligence tools can monitor server performance, network congestions, and disk utilization to get the bets performance out of every server. With these types of AI tools, organizations can leverage advanced predictive analytics to track power levels and identify potential defective areas in the systems.

For example, startup Granulate is leveraging AI and ML to eliminate all bottlenecks for “hyper-increased” server utilization while also improving quality of service. Granulate’s platform leverages machine learning algorithms to optimize Linux server environments running on-premises or in the cloud. The platform uses agents that install machine algorithms to continuously optimize deployments across a server environment.

AI And ML Change Data Center Staffing Requirements

Greater adoption of AI tools and ML solutions are automating a variety of tasks including from equipment status monitoring and temperature management and to ventilation and cooling systems. The manual tasks conducted by data center operators and teams inside these facilities are become less and less due to artificial intelligence and machine learning, meaning the amount of people needed to manage an entire data center is arguable smaller than ever before.

With AI and ML tools taking over a slew of pervious human-led tasks, organizations can reduce staffing shortages. Additionally, jobs such as essential tech support and administration are become obsolete as organizations introduce more AI.

This means that customer who implement AI and ML tools can have more employees working on higher level tasks and providing better value-added services to customers. Companies can witness a better return on investment (RIO) from AI and ML products when their employees can focus strictly on providing business outcomes to customers.

AI And ML Tools For Facial Recognition

Both machine learning and artificial intelligence are creating new data center services around facial recognition, which has surged since the COVID-19 pandemic as many companies are tracking employees like never before such as for identify badges and temperature checks before entering a facility.

Facial recognition technology can leverage ML or AI algorithms to find, capture, store and analyze facial features in order to match them with images of individuals in a database.

For example, PXL Vision provides solutions for the automation and enhancement of digital identity verification and customer onboarding through software powered by artificial intelligence and machine learning technologies. The company offers solutions across all phases of identity verification and validation – from making sure a person logging in is a true human to verify documents.

‘Lights Out’ Data Centers Are Now Possible, But Hard To Implement

With the rapid innovation of machine learning and artificial intelligence tools, some companies are now trying out the idea of fully automating data centers for periods of time. The idea of a “lights out” fully autonomous data center operation has been spoken about for years, but only now – thanks to AI and ML innovation – can this become a reality.

For example, data center company said its business premise was based on providing “lights out” data centers. Although EdgeConneX isn’t entirely “lights out” yet, the company has remote control physical security and uses remote monitoring and sensors to track operations, meaning employees are deployed to sites only when needed.

The goal is to get as many energy-efficient and technology effective solutions inside a data center that can be remotely monitored by data center infrastructure management (DCIM) software. By using AI, ML and potentially robots to eliminate the need for employees in the physical space, data centers could optimize oxygen levels and physical security to reduce energy, completely cut out the need for lighting, create more efficient cooling designs, and increase IT rack capacity and heights.

However, the ability to various AI and ML tools to seamlessly interoperate with each other algorithmically for critical decision making is a major challenge, not to mention the social and economic impacts.

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