As organizations transform their IT applications and adopt cloud-native architectures, scaling seamlessly while minimizing resource overheads becomes critical. DevOps teams can play a pivotal role in achieving this by embracing automation across various facets of the service delivery process.
Automation shines in areas such as infrastructure provisioning and scaling, continuous integration and delivery (CI/CD), testing, security and compliance, but the practice of automated root cause analysis remains elusive.
While automation aids observability data collection and data correlation, understanding the relationships between cause and effect still requires the judgment and expertise of skilled personnel. This work falls on the shoulders of developers and SREs who have to manually decode the signals – from metrics, traces and logs – in order to get to the root cause when the performance of services degrades.
Individual incidents can take hours and even days to troubleshoot, demanding significant resources from multiple teams. The consistency of the process can also vary greatly depending on the skills that are available when these situations occur.
Service disruptions can also have significant financial consequences. Negative customer experiences directly impact revenue, and place an additional resource burden on the business functions responsible for appeasing unhappy customers. Depending on the industry you operate in and the type of services you provide, service disruptions may result in costly chargebacks and fines, making mitigation even more crucial.
Quantifying the load of manual root cause analysis on IT teams
Shining a Light on the Root Cause Analysis Problem in DevOps
While decomposing applications into microservices through the adoption of cloud-native architectures has enabled DevOps teams to increase the velocity with which they can release new functionality, it has also created a new set of operational challenges that have a significant impact on ongoing operational expenses and service reliability.
Increased complexity: With more services comes greater complexity, more moving parts, and more potential interactions that can lead to issues. This means diagnosing the root cause of problems becomes more difficult and time-consuming.
Distributed knowledge: In cloud-native environments, knowledge about different services often resides in different teams, who have limited knowledge of the wider system architecture. As the number of services scales, finding the right experts and getting them to collaborate on troubleshooting problems becomes more challenging. This adds to the time and effort required to coordinate and carry out root cause analysis and post incident analysis.
Service proliferation fuels troubleshooting demands: Expanding your service landscape, whether through new services or simply additional instances, inevitably amplifies troubleshooting needs which translate into more resource requirements in DevOps teams for troubleshooting overtime.
Testing regimes cannot cover all scenarios: DevOps, with its CI/CD approach, releases frequent updates to individual services. This agility can reveal unforeseen interactions or behavioral changes in production, leading to service performance issues. While rollbacks provide temporary relief, identifying the root cause is crucial. Traditional post-rollback investigations might fall short due to unreproducible scenarios. Instead, real-time root cause analysis of these situations as they happen is important to ensure swift fixes and prevent future occurrences.
Telling the Story with Numbers
As cloud-native services scale, troubleshooting demands also grow exponentially, in a similar way to compounding interest on a savings account. As service footprints expand, more DevOps cycles are consumed by troubleshooting versus delivering new code, creating barriers to innovation. Distributed ownership and unclear escalation paths can also mask the escalating time that is consumed by troubleshooting.
Below is a simple model that can be customized with company-specific data to illustrate the challenge in numbers. This model helps paint a picture of the current operational costs associated with troubleshooting. It also demonstrates how these are going to escalate over time, driven by the growth in cloud-native services (more microservices, serverless functions, etc).
The model also illustrates the impact of efficiency gains through automation versus the current un-optimized state. The gap highlights the size of the opportunity available to create more cycles for productive development while reducing the need for additional headcount into the future, by automating troubleshooting.
This model illustrates the costs and impact of efficiency gains that can be achieved through automated root cause analysis
Beyond the cost of human capital, there are a number of other costs that have a direct impact on troubleshooting costs. These include the escalating costs of infrastructure and third-party SaaS services, dedicated to the management of observability data. These are well publicized and highlighted in a recent article published by Causely Founding Engineer Endre Sara that discusses avoiding the pitfalls of escalating costs when building out Causely’s own SaaS offering.
While DevOps have a cost, they pale in comparison to the financial consequences of service disruptions. With automated root cause analysis, DevOps teams can mitigate these risks, saving the business time, money and reputation.
Future iterations of the model will account for these additional dimensions.
If you would like to put your data to work and see the quantifiable benefits of automated root cause analysis in numbers, complete the short form to get started.
Translating Theory into Reality
Did you know companies like Meta and Capital One automate root cause analysis in devops, achieving 50% faster troubleshooting? However, the custom solutions built by these industry giants require vast resources and deep expertise in data science to build and maintain, putting automated root cause analysis capabilities out of reach for most companies.
The team at Causely are changing this dynamic. Armed with decades of experience – applying AI to the management of distributed systems, networks, and application resource management – they offer a powerful SaaS solution that removes the roadblocks to automated root cause analysis in DevOps environments. The Causely solution enables
- Clear, explainable insights: Instead of receiving many notifications when issues arise, teams receive clear notifications that explain the root cause along with the symptoms that led to these conclusions.
- Faster resolution times: Teams can get straight to work on problem resolution and even automate resolutions, versus spending time diagnosing problems.
- Business impact reduction: Problems can be prevented, early in their cycle, from escalating into critical situations that might otherwise have resulted in significant business disruption.
- Clearer communication & collaboration: RCA pinpoints issue owners, reducing triage time and wasted efforts from other teams.
- Simplified post-incident analysis: All of the knowledge about the cause and effect of prior problems is stored and available to simplify the process of post incident analysis and learning.
Wrapping Up
In this article we discussed the key challenges associated with troubleshooting and highlighted the cost implications of today’s approach. Addressing these issues is important because the business consequences of today’s approach are significant.
- Troubleshooting is costly because it consumes the time of skilled resources.
- Troubleshooting steals time from productive activities which impacts the ability of DevOps to deliver new capabilities.
- Service disruptions have business consequences: The longer they persist, the bigger the impact to customers and business.
If you don’t clearly understand your future resource requirements and costs associated with troubleshooting as you scale out cloud-native services, the model we’ve developed provides a simple way to capture this.
Want to see quantifiable benefits from automated root cause analysis?
Turn “The Optimized Mode of Operation” in the model from vision to reality with Causely.
The Causely service enables you to measure and showcase the impact of automating RCA in your organization. Through improved service performance and availability, faster resolution times, and improved team efficiency, the team will show you the “art of the possible.”
Related Resources
- Watch the video: Mission Impossible: Cracking the Code of Complex Tracing Data
- Read the blog: Eating Our Own Dog Food: Causely’s Journey with OpenTelemetry & Causal AI
- Request a demo: See Causely in action