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What is meant by Runbook Automation?

Runbook Automation is the process of automating runbook procedures. With the ever-transforming IT industry, IT organizations' roles, and responsibilities are becoming more diverse and more complex, resulting in the need for automation. And with the advancements in AI, the concept of intelligent automation has emerged in recent years.

Runbooks are predefined procedures and best practices for solving common IT problems such as troubleshooting, diagnostics, and data management. However repetitive, these tasks become complex due to the sheer number of procedures involved. Hence the need for automation.

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As with most technologies, Runbook Automation (RBA) is also following the trend of integrating machine learning (ML) and artificial intelligence (AI) so that they can move from a rigid rule-based approach where the entire runbook is manually programmed in an if-else manner. ML and AI have changed this and have made RBA cognitive, where decisions are taken by knowledge representation, learning, and reasoning with data.

Why Automate Runbooks?

  • RBA is an efficient way of solving complex IT problems that has a repetitive nature. It enables IT teams, to reduce Meantime to repair (MTTR), increase the interval of Meantime between failures (MTBF), and thus save time, money, and resources.
  • RBA can even help dev-ops teams to do routine maintenance, debugging, and troubleshooting of the code.
  • RBA can make an organization self-sufficient and provide tools and knowledge of know-how to individuals who are not experts in the field.

What are the benefits of Runbook Automation?

  • Minimize application downtime: RBA offers organizations to cut short implementation time on procedures when the system requires specific actions after encountering issues. RBA does this by automating several steps required to solve and mitigate the issue.
  • Simplify routine maintenance: One of the challenges that IT organizations face is to have all the proper tools to mitigate the problem in one place. RBA helps catalog all the necessary tools that can be used or triggered automatically when needed.
  • Automated update and enrichment: Another benefit of RBA is runbook enrichment. A runbook that has not been run or up to date becomes worthless. RBA can improve the documentation with automation and enrich it with newer features simultaneously.
  • Self-service operations: RBA improves communications between IT teams as it may automate common tasks mentioned in emails, messages, and tickets (for IT issues) and run the procedures independently.
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Some use cases of Runbook Automation

  • Automate routine maintenance: DevOps use Runbooks to automate routine maintenance and respond to error messages and alerts. This makes finding and fixing bugs relatively more straightforward. Developers can get detailed runbook procedures to prepare for and resolve glitches quickly.
  • Incident response and Service requests: Runbooks also help Site Reliability Engineers (SRE) solve incidents faster, resulting in fewer escalations, reduced MTTR, and better productivity for development teams. Runbooks also help fulfill service requests by decreasing average wait times and reducing interruptions.
  • Operational automation: Runbooks help network operation centers (NOC) monitor and resolve network issues faster. They also help exchange information while outsourcing network liabilities to 3rd party organizations.
  • Improves Security: Runbook automation can manage better delegation of incident resolutions; in cases of security issues, RBA can help properly delegate issues to proper experts, thus minimizing escalations.
  • Optimizing IT operations and troubleshooting: One of the everyday use cases of RBA is by IT Ops to automate routine maintenance and troubleshooting. They also provide the IT team with proper and consistent guides on standard operating procedures.
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Challenges that AI integration with RBA can solve

  • Complex and evolving IT landscape: The IT industry is changing rapidly; with that, the needs and challenges are also evolving. AI-powered RBA can be robust in their functioning as AI can update the documentation more efficiently, thus keeping the runbooks up-to-date.
  • Short and SMEs and specialists: Though one of the world's largest industries, there is still a significant shortage of specialist and domain experts. AI-based runbooks can help in incident resolving and completing repeating tasks efficiently. This can relieve some pressure on specialists, and they can devote time and resources to the problems that truly require their attention.
  • Delays in isolating and resolving IT faults: AI-based runbooks can help reduce delays caused during isolating issues so that they can be forwarded to proper specialists or personnel. AI-based runbooks can classify and prioritize the issues on their own.
  • Business impact due to frequent outages and increased downtimes: AI-powered RBAs can minimize frequent outages caused by IT issues by automating resolution and prioritization and also minimizes downtime by managing and resolving faults and mistakes quicker.
  • Heavy reliance on people and tacit knowledge: AI-powered runbooks can remain up-to-date without oversight and provide the know-how and tools to IT personnel who are not domain experts to minimize heavy reliance on domain experts.
  • Inconsistent and Variable quality of operations: Detection of inconsistencies in runbook procedures and management of variability in the quality of operations can be quickly addressed using AI-based runbooks, which can detect such inconsistencies and provide appropriate countermeasures.
  • Risk of human errors: AI-based runbook automation eliminates human involvement and supervision, thus eliminating the risk of human errors.

AI-powered RBA vs. Rule-based RBA

AI-based Rule-based
AI based runbooks are based on probabilistic models which can evolve and adapt according to the data it is trained upon. Rule-based runbooks are deterministic in nature. They do not have the ability to evolve with changing circumstances. They lack any form of robustness.
AI based rule runbooks are easy to scale. Rule-based runbooks are often not suited for scalability.
AI systems that utilize machine learning algorithms need large amounts of data in order to build models. Rule-based runbooks do not require scores of data in order to be developed.
AI based runbooks do not need domain experts to be formed as they can learn on their own. Rule-based runbooks constant supervision of domain experts from the point of development to repeated updating.
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Conclusion

With such a diverse need for IT operations and the increasing responsibilities of IT Ops, There is an urgent need for better Runbook Automations so that the very backbone of the IT industry can function seamlessly. This can be achieved by integrating Artificial intelligence into running Runbooks to make them more efficient and robust. Thus solving many issues that pose serious setbacks for the IT industry.

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