IT automation is a core tenet of a successful SRE strategy. Traditionally, however, IT automation has been employed in an ad hoc manner with custom scripts and domain-specific tooling. While these implementations can have positive impacts on system reliability, their failure to span technology domains and leverage modern development capabilities (such as AI and machine learning) make them incomplete and limited solutions in that regard.
Modern development organizations that embrace IT automation tactics and tooling that support an effective SRE strategy can avoid the pitfalls of the approaches mentioned above. Keep reading for details on how to employ IT automation in a way that fuels SRE success.
The Benefits of IT Automation in SRE
Over the past decade, more and more software development organizations have embraced SRE in an effort to build scalable and highly reliable systems. During this time, IT automation has become an increasingly important aspect of this process. Consider the following ways in which IT automation can help improve system reliability.
Reducing MTTA and MTTR to Improve Availability
In almost every scenario, automated data collection and analysis will result in the identification of issues within an application or its infrastructure in a more timely manner than the traditional process of waiting for an engineer or customer to stumble upon the problem and then notify the correct personnel. This reduces mean time to acknowledgement. In other words, the necessary parties within the IT organization will learn about issues within their systems sooner if automation is employed.
The sooner problems are discovered, the sooner they can be resolved (thus reducing mean time to resolution). This limits their impact on end users, which is important for maintaining good relationships with customers and a positive reputation in the marketplace. In addition, it is critical for good SRE, because it decreases system downtime and improves reliability.
Thorough Insights Lay the Groundwork for Permanent Solutions
IT automation is the practice of automating system performance and security monitoring. Modern monitoring platforms can provide deep insights into the performance and stability of these systems in a manner that provides engineers with the information necessary to implement more complete and permanent fixes for issues that threaten system availability. From a system reliability perspective, the implementation of thorough, data-driven fixes enables a process of continuous improvement. In other words, as the system matures, it will become more and more stable (and experience fewer and fewer of those pesky recurring issues).
Characteristics of an IT Automation Strategy that Enables Successful SRE
It’s clear that IT automation plays a critical role in ensuring system reliability and effective SRE. But in order to achieve this level of effectiveness, organizations must leverage platforms and tools that implement automation in a way that enables these benefits. Let’s dig into the aspects of an IT automation strategy that are key for successful SRE.
Achieving End-To-End Visibility with Automation that Spans Technology Domains
Effective IT automation requires monitoring in a way that provides end-to-end visibility into the entire system through a single pane of glass. This is done by centralizing data from different sources (applications, infrastructures, databases, etc.) and then correlating this centralized data through analysis. In this way, IT folks can see everything that is occurring within their system when a problem arises.
With respect to SRE, end-to-end visibility helps enable a more effective and efficient process of cause analysis. By centralizing the data and analysis for all system components, organizations can eliminate (or reduce) the challenges traditionally experienced by teams that consume data from various domains in a more fragmented or siloed manner.
Leveraging AI to Improve Organizational Responsiveness
In addition to enabling visibility into all layers of the system, modern IT automation platforms should leverage AI and machine learning algorithms to make the most of the data collected from the various components of their system. In other words, software organizations that want to ensure that their systems are scalable and highly reliable should leverage platforms empowered with AIOps capabilities.
Modern IT automation platforms utilize AI and machine learning algorithms to correlate and evaluate data gathered from various system components. The data-driven insights gleaned from this analysis can bring value to an organization in a few different ways:
They provide developers with insights that enable them to better direct their development efforts in order to produce the greatest value to the organization.
They help improve system performance (such as database tuning guidance and infrastructure configuration suggestions) by providing intelligent recommendations based on data analysis.
In some instances, IT automation solutions powered by AIOps can leverage AI and machine learning capabilities to take the process of issue remediation completely out of the hands of IT operations personnel, thereby achieving automated remediation. At this stage of the game, automated remediation is typically employed for more basic system issues. For example, in the instance of a service interruption due to a minor problem, the system can now make data-based decisions to automatically remediate the problem and get back to a healthy and stable state.
At its core, SRE is about ensuring system reliability. For a system to be considered reliable, it must be available and usable (i.e. not experiencing significant latency or a high error rate). By now, there is no doubt that IT automation can help to ensure available and usable systems.
Modern IT automation helps derive insights that save time up front (including incident discovery as well as data gathering and analysis) and on the back end (including root cause analysis and remediation).
Truly useful IT automation tools must span technology domains and provide the end-to-end visibility that is necessary for organizations to streamline the traditionally time-intensive processes of root cause analysis and incident remediation.
AI and machine learning are quickly becoming essential in the realm of IT automation. When data can be collected from the entire digital chain and analyzed via machine learning algorithms, organizations can take advantage of intelligent recommendations and automated remediation capabilities that help reduce downtime and improve system performance.