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What is AIOps?

AIOps (Artificial Intelligence for IT Operations) is an emerging IT technology that applies artificial intelligence to IT operations to help enterprises intelligently manage infrastructure, networks, and applications to achieve performance, elasticity, productivity, uptime, and in some cases maintaining security. AIOps shifts traditional threshold-oriented alerting and manual processes into systems that leverage AI and machine learning, enabling businesses to more closely monitor IT assets and predict negative events and impacts.

Modern IT deployments must deal with increasingly rapid and incremental data demands. This data is often unstructured and live-streamed from resource silos in vast networks. AIOps platforms help IT operations (ITOps) teams leverage the volume, variety, and velocity of big data. AIOps is an artificial intelligence application for enhancing IT operations. AIOps uses big data, analytics, and machine learning capabilities to perform various tasks:

• Collect and aggregate the vast and growing volume of operational data generated by multiple IT infrastructure components, applications, and performance monitoring tools.
• Intelligently filter signals from the noise to identify important events and patterns related to system performance and availability issues.
• Diagnose and report the primary cause to IT for rapid response and remediation, improving automated problem resolution, and reducing the frequency of human intervention.

AIOps replaces multiple independent manual IT operations tools with a single intelligent, automated IT operations platform, enabling IT operations teams to respond more quickly and even more proactively to slowdowns and service disruptions, while also significantly reducing work.

Why do you need AIOps?

Most organizations are moving from traditional infrastructures consisting of separate static physical systems to dynamic hybrid architectures that include on-premises, managed cloud, private cloud, and public cloud environments. Applications and systems in these environments generate ever-increasing amounts of data, with the average enterprise IT infrastructure generating two to three times more data per year for IT operations. Traditional domain-based IT management solutions cannot keep up with the volume growth. They cannot efficiently and intelligently sort out major events from such vast amounts of data. They cannot establish data associations between disparate but interdependent environments. They also fail to provide the immediate insights and predictive analytics IT teams need to respond to problems fast enough to meet user and customer service levels.

Therefore, AIOps technology has been developed, which can display performance data and dependencies of all environments, analyze the data to capture important events related to slowdowns or operation interruptions, and automatically send relevant warning reminders, problem causes, and suggested solutions to IT personnel.

91ÊÓƵ¹ÙÍø does AIOps work?

Learn about the role each AIOps component technology (big data, machine learning, and automation) plays in the process.

1. AIOps will use a big data platform to bring siloed IT job data into one place.

• Process performance and event data
• Stream instant job events
• System logs and metrics
• Network data, including packet data
• Incident-related information and questions
• Related documents

• To separate critical event alerts from noise: AIOps uses analytics to tease out IT operational data and separate signals (alerts of major anomalies) from noise.
• Identify the main reasons and propose solutions: AIOps leverages industry-specific or environment-specific algorithms to correlate anomalous events with other event data in the environment to focus on the cause of operational disruptions or performance issues and recommend remedial actions.
• Automated responses, including immediate proactive solutions: AIOps can at least automatically route alerts and suggested solutions to the appropriate IT team, or even create a response team based on the nature of the problem and solution. The results of machine learning can be processed to trigger an automatic system response to deal with the problem immediately before the user even realizes that there is a problem.
• Continuous learning to improve your ability to deal with future problems: Based on the results of the analysis, machine learning capabilities can change algorithms, or build new ones, to identify problems earlier and suggest more efficient solutions. AI models can also help systems understand and adapt to changes in the environment, deploying or reconfiguring appropriate infrastructure.

91ÊÓƵ¹ÙÍø can AIOps automation simplify traditional jobs?

• Observed:
  The main cause of the downtime must be identified and dealt with by the appropriate personnel. The AIOps platform automatically captures records, metrics, alerts, events, and other required data to understand the operating reasons behind application events. Instead of relying on manual work to extract and interpret information from disparate data sources, the platform can consolidate and categorize all data.
• Input:
  Includes analyzing monitoring data and diagnosing the root cause of downtime. Information relevant to solving the problem is considered in context and sent to the equipment personnel best suited for the operation. AIOps tools can perform a risk analysis, automate responsibility communication, and prepare relevant data for IT operators.
• Implement:
  The Direct Responsible Person (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 the next time an AIOps tool detects a specific problem.

AIOps can help IT operations respond to disasters faster and minimize recovery time-to-time objective (RTO) and recovery point objective (RPO) through partially automated processes.

What are the advantages of AIOps?

The overall benefit of AIOps is that it allows IT operations to automatically filter from alerts across multiple IT operations tools to identify, address, and resolve slowdowns and disruptions faster than manual filtering.

• Achieve faster mean time to resolution (MTTR): By de-cluttering IT operations and correlating operational data across multiple IT environments, AIOps can identify major causes and propose solutions faster and more accurately than humans.
• From reactive to proactive to predictive management: Because AIOps never stops learning, it continually improves to better identify less urgent alerts or signals associated with more urgent situations. This means it can provide predictive alerts that allow IT teams to address potential issues before they cause slowdowns or disruptions.
• Modernize IT operations and IT operations teams: Instead of being bombarded with every alert in every environment, AIOps teams will only receive alerts that meet certain service level thresholds or parameters, all together with all the necessary context definitions to make the best diagnosis and take the best and fastest corrective action. The more AIOps learns and becomes more automated, the better it can keep running with less human effort, freeing IT operations teams to focus on work of higher strategic value to the business.

AIOps use cases:

• Digital Transformation: Digital transformation creates IT complexities (e.g., multiple environments, virtualized resources, dynamic infrastructure) that AIOps is designed to address. The right AIOps solution gives organizations more freedom and flexibility to transform according to strategic business goals without worrying about IT workloads.
• Cloud Adoption/Migration: Cloud adoption is an incremental process, and this creates a hybrid multi-cloud environment (private cloud, public cloud, multiple vendors) where multiple interacting dependencies may change too quickly and frequently to be documented. By clearly showing these interdependencies, AIOps can dramatically reduce the operational risk of cloud migration and hybrid cloud approaches.
• DevOps adopts: DevOps accelerates development by improving the ability of development teams to deploy and reconfigure infrastructure, but IT must still manage that infrastructure. AIOps provides the visibility and automation IT needs to support DevOps without adding additional administrative labor.