AIOps is an emerging IT technology that utilizes artificial intelligence to simplify IT operations management and accelerate and automate problem resolution in complex modern IT environments. Today, we will explore what AIOps is, why it is needed, how AIOps works, how AIOps automation simplifies traditional work, the advantages of AIOps, and the use cases of AIOps.
What is AIOps?AIOps (Artificial Intelligence for IT Operations) is an emerging IT technology that applies artificial intelligence to IT operations, helping enterprises intelligently manage infrastructure, networks, and applications to achieve performance, resilience, productivity, uptime, and in some cases, maintenance security. AIOps transforms traditional threshold-based alerts and manual processes into systems that leverage AI and machine learning, enabling businesses to monitor IT assets more closely and predict negative events and impacts. In summary, AIOps combines big data, AI, or machine learning capabilities to help enterprises comprehensively support digital business.Modern IT deployments must handle rapidly increasing data demands. This data is often unstructured and transmitted in real-time from repositories across a vast network. AIOps platforms help ITOps teams leverage the volume, variety, and velocity of big data. AIOps is an artificial intelligence application used to enhance IT operations, employing big data, analytics, and machine learning capabilities to perform various tasks:Collecting and aggregating massive and continuously growing operational data generated by multiple IT infrastructure components, applications, and performance monitoring tools;Intelligently filtering signals from noise to identify significant events and patterns related to system performance and availability issues;Diagnosing and reporting root causes to the IT department for quick response and remediation, improving automated problem-solving solutions, and reducing the frequency of human intervention.AIOps replaces multiple standalone manual IT operations tools with a smart, automated IT operations platform, enabling IT operations teams to respond more quickly and proactively to slowdowns and service interruptions while significantly reducing workload.
Why is AIOps Needed?
Most organizations are transitioning from traditional infrastructures composed of isolated static physical systems to dynamic hybrid architectures that include on-premises, hosted cloud, private cloud, and public cloud environments. The volume of data generated by applications and systems in these environments is continually increasing, with enterprise IT infrastructure generating two to three times the amount of data for IT operations each year compared to before. Traditional domain-based IT management solutions cannot keep pace with the growth in business volume, cannot efficiently and intelligently sift through significant events from such vast amounts of data, cannot establish data correlations between completely different yet interdependent environments, and cannot provide the instant insights and predictive analytics that IT teams need to respond to issues quickly enough to meet user and customer service levels.Thus, AIOps technology was developed to display performance data and correlations across all environments, analyze data to capture significant events related to slowdowns or operational interruptions, and automatically send relevant alerts, root causes, and suggested solutions to IT personnel.
How Does AIOps Work?
Understanding the role of each AIOps component technology (big data, machine learning, and automation) in this process.AIOps will use big data platforms to centralize isolated IT operational data in one place:Process performance and event data;Streaming real-time job events;System logs and metrics;Network data, including packet data;Event-related information and issues;Relevant documentation.AIOps will apply focused analytics and machine learning capabilities:Separating critical event alerts from noise:AIOps uses analytics to sift through IT operational data and distinguish signals (significant anomaly alerts) from noise.Identifying root causes and proposing solutions:AIOps utilizes algorithms specific to industries or environments to correlate anomalous events with other event data in the environment, focusing on the causes of operational interruptions or performance issues and recommending remedial actions.Automated responses, including immediate proactive solutions:AIOps can at least automatically send alerts and suggested solutions to the relevant IT teams, and even create response teams based on the nature of the issues and solutions. The results of machine learning can be processed immediately before users are aware of the problem to trigger automated system responses to address the issue.Continuous learning to improve handling of future issues:Based on analytical results, machine learning capabilities can change algorithms or build new algorithms to detect problems earlier and propose more effective solutions. AI models can also help systems understand and adapt to changes in the environment, deploying or reconfiguring appropriate infrastructure.
How Does AIOps Automation Simplify Traditional Work?
Observation:The primary causes of downtime must be identified and addressed by the appropriate personnel. The AIOps platform automatically captures records, metrics, alerts, events, and other necessary data to understand the operational reasons behind application events. This platform can integrate and classify all data rather than relying on manual extraction and interpretation of information from different data sources.Input:This includes analyzing monitoring data and diagnosing the root causes of downtime. Information related to problem resolution must be considered in the context of the situation and sent to the personnel best suited for the operation. AIOps tools can perform risk analysis, automate responsibility communication, and prepare relevant data for IT operators.Implementation:The Directly Responsible Individual (DIR) is responsible for resolving issues and fixing application services. Programming languages, runbooks, and Application Release Automation (ARA) can also be created to run automatically when the AIOps tool detects specific issues next time.AIOps can help IT operations departments respond to disasters faster and minimize Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO) through partially automated processes.
What Are the Advantages of AIOps?
The overall advantage of AIOps is that it allows IT operations personnel to automatically filter alerts from multiple IT operations tools to identify, address, and resolve slowdowns and interruptions faster than manual filtering.Achieving Faster Mean Time to Resolution (MTTR):By organizing IT operations and correlating operational data across multiple IT environments, AIOps can identify root causes and propose solutions faster and more accurately than humans.From Reactive to Proactive to Predictive Management:Because AIOps never stops learning, it continuously improves its ability to better identify less urgent alerts or signals related to more urgent situations. This means it can provide predictive alerts, enabling IT teams to address potential issues before they lead to slowdowns or interruptions.Modernizing IT Operations and IT Operations Teams:AIOps teams are not bombarded with every alert in every environment, but only receive alerts that meet specific service level thresholds or parameters to make the best diagnoses and take the best and fastest corrective actions. The more AIOps learns and the higher the degree of automation, the more it can operate continuously with less manpower, allowing IT operations teams to focus on work that has higher strategic value for the business.
AIOps Use Cases:
Digital Transformation:Digital transformation brings IT complexity (e.g., multiple environments, virtualized resources, dynamic infrastructure), and AIOps solutions provide enterprises with greater freedom and flexibility to transform according to strategic business objectives without worrying about IT workloads.Cloud Adoption/Migration:Cloud adoption is a gradual process that creates a hybrid multi-cloud environment (private cloud, public cloud, multiple vendors), where multiple interdependencies may change too quickly and frequently to document. By clearly displaying these interdependencies, AIOps can significantly reduce the operational risks of cloud migration and hybrid cloud approaches.DevOps Adoption:DevOps accelerates development by enhancing the ability of development teams to deploy and reconfigure infrastructure, but it still has to manage that infrastructure. AIOps provides the visibility and automation needed to support DevOps without adding extra management personnel.