IT service security has many layers. The IT security layer; firewalls, intrusion detection and access controls. The infrastructure layer; power, network, server health and cooling. And, most important, the people layer. The right people with the right processes, tools and measures to ensure everything else is in working order. Artificial intelligence (AI) will by far have the biggest impact on the tools and measures that people use by amplifying capabilities, streamlining processes and increasing efficiencies.
AI and deep learning will become a necessity in parsing and analyzing the mountain of data generated within a data center to more effectively manage service delivery while mitigating risks like outages. This stems from the recent transformation in how we deliver application workloads.
Too much data?
In the last 10 years, we’ve moved from mostly single server single applications to distributed applications that run in containers. These are now being delivered by micro-services running on-premise and in the cloud–all managed by automation tools. Infrastructure has become part of the application, while other applications have become part of the infrastructure. If you are using a platform like Amazon S3 or Google Maps as an integral component of your service delivery, then you are experiencing this transformation first-hand.
The resulting impact on data center management is significant with power and cooling becoming just a fraction of what needs regular attention. Environmental controls, physical devices, virtual machines and public clouds all need to be monitored and managed round-the-clock to achieve efficiencies in cost and performance. Understanding where and when to move specific workloads becomes paramount.
AI and deep learning will become a necessity in parsing and analyzing the mountain of data generated within a data center
The amount of data an enterprise collects, monitors and analyzes today to ensure business continuity has exploded. Consider the data generated just from sensors, applications, access control systems, power distribution units, UPS, generators, and solar panels. Add to that external data sources like application vulnerability information, power rates and weather forecasts. Robust data center infrastructure management (DCIM) tools are needed to store all of this data, analyze it and turn it into actionable intelligence. You can try to compartmentalize some of this, but it is becoming increasingly difficult.
AI and deep learning are becoming integral in data center and critical infrastructure management. Here are some of the more notable areas:
- Situational awareness
Active dashboards with trends, correlations analysis and recommended actions.
- Preventive maintenance
Deep learning used to identify and correlate data that predicts a failure in power, storage or network connection. This allow operators to mobilize and pro-actively move workloads to safer zones, while maintenance is being performed.
- Root cause analysis
Machine learning used to trace the failure of several services to a root cause. This becomes learned and used for future preventive maintenance.
- Network security and intrusion detection
Machine learning and deep neural networks used to spot unusual patterns in application sensors, access control systems and network systems–and provide better signal-to-noise and pro-active mitigations. Learning neural networks are used to continuously improve the enterprise’s security posture and ability to manage related issues.
A “Narrow AI” equipped with various automated mitigation techniques and resulting actions similar to a car applying the brakes if it sees an imminent collision.
Deep neural networks and machine learning algorithms will improve over time, allowing for higher efficiency and performance to match fast growing application workloads. With all of this on the horizon, there’s little doubt that AI will have a massive impact on how enterprises manage their data center.
Rami Jebara is the CTO of Tuangru