AIOps use cases address complexity in virtual data centers
The ever-growing complexity of virtual infrastructures is making management a struggle, but an AIOps platform can enable admins to transform raw data into useful information.
Artificial intelligence for IT operations, or AIOps, use cases are plentiful, but IT administrators might find the most significant benefits using machine learning technologies to optimize virtual resources.
An organization's IT infrastructure generates massive amounts of system data, ranging from application latency rates to hardware temperatures. As the infrastructure grows and becomes more complex, so too does the management process, which often results in siloed operations and fragmented information. Even if IT has the capacity to centralize disparate resources, admins might still lack the tools comprehensive enough to orchestrate efforts across all the domains.
AIOps can address these issues by correlating and analyzing the wide breadth of system data with machine learning and then determine the appropriate actions. AI refers broadly to the simulation of human intelligence, and it uses learning and reasoning to arrive at conclusions based on inputted data. AI is self-correcting and continuously improves as operators add more data.
Machine learning is a subset of AI that applies specifically to computers. Machine learning enables a computer to learn from inputted data, predict outcomes and run operations without specific programming. At the heart of this technology is a set of intelligent algorithms that performs in-depth statistical analysis against the data. AIOps use cases emerge from the resulting patterns, as the technology automatically adjusts its actions and evolves as data becomes available.
IT teams have begun to incorporate machine learning and other AI technologies into management strategies that address complex infrastructures. This trend uses AI concepts, such as big data analytics, to automatically correlate data across millions of data points, identify issues and provide ways to address those issues in as close to real time as possible.
AIOps platforms offer automation and clarification
AIOps use cases are compelling because the technology can augment or replace a wide range of IT operations tools. AIOps can reduce mundane administrative tasks, but it can also prevent outages, filter out noise from legitimate issues, reduce siloed operations, perform more accurate root cause analysis and generally streamline IT operations.
An AIOps platform uses a variety of strategies to facilitate administrative processes, but it begins with comprehensive data collection from data points across all the domains. The data can include log files, help desk ticketing services and output from monitoring tools. The platform monitors systems across all virtual and nonvirtual environments, using big data technologies to aggregate and organize the data into useful formats.
From there, the AIOps platform analyzes the data with machine learning algorithms and other AI technologies to correlate information, uncover patterns, detect anomalies, determine root causes and identify causal relationships between servers, systems and platforms.
An AIOps platform can then use the results of its analysis to automate and orchestrate operations, which triggers actions based on key points. For example, if an application running in a VM needs more processing power, an AIOps platform can automatically allocate the necessary resources to that application.
A comprehensive AIOps platform can also provide tools for admins to generate reports and visualizations that can help them identify issues, track changes to the environment, make decisions and provide general insight into the IT infrastructure.
Virtualization optimization tops AIOps use cases
The way virtualization platforms abstract resources can create IT infrastructure management challenges. Although virtualization can be highly beneficial for hardware utilization and workload management, the environment's hidden nature can also make it more difficult to pinpoint potential issues.
Additionally, virtualization can cause a proliferation of VMs that creates significantly more data points than physical resources would, which exponentially increases the amount of system data.
At the same time, capacity for virtual environments is difficult; admins often over or under provision resources. Many IT management tools aren't comprehensive or accurate enough to effectively monitor and manage both virtual and nonvirtual environments.
Addressing these management limitations is a primary AIOps use case, as the technology can improve resource utilization, capacity planning, threat detection, anomaly detection and storage management. AIOps platforms can also correlate data points across all the environments and address the limitations of many management tools, all of which can lead to better resource usage.
For example, ScienceLogic offers an AIOps platform that provides visibility into the entire physical and virtual infrastructure, which makes it possible to track compute resources wherever they are and optimize application performance and availability. Dynatrace offers a similar service that enables admins to better understand their infrastructure, detect dependencies and anomalies, and identify root causes.
SIOS iQ -- another AIOps service -- is similar, but it's specific to VMware virtual environments. It can identify resource concerns, such as idle VMs or unnecessary snapshots, while defining, prioritizing and resolving issues.
AIOps is still a relatively young field, especially when it comes to resource management across virtual and nonvirtual environments. Given the ever-growing complexities that come with IT infrastructures, however, AIOps might be one of the most promising technologies on the horizon. The proliferation of containers, microservices, serverless computing and other technologies that complicate modern environments makes AIOps use cases even more compelling.