Cloud Canaries emerged from stealth today to provide a set of lightweight agents that leverage a neural network to detect issues, monitor performance in real-time and identify how to resolve software engineering issues.
Company CEO Mark Callahan said Intelligent Canaries are lightweight agents that DevOps teams can deploy that invoke a Cloud Intelligence platform the company has developed. Rather than having to instrument every IT environment, Intelligent Canaries are designed to be deployed as needed, he added.
That approach eliminates the need to rely on an observability platform that needs to continuously collect massive amounts of log data to surface actionable insights. The challenge is the total cost of that approach to observability requires major investments. In contrast, Intelligent Canaries are a lighter-weight alternative that leverages neural networking technology embedded in a cloud service at a fraction of the cost of an observability platform, said Callahan.
Intelligent Canaries provide the same real-time feedback on system performance as an observability platform that DevOps teams use to monitor key metrics and ensure systems operate within acceptable parameters.
DevOps teams, however, can also use Intelligent Canaries to autonomously refine processes, performance and reliability of workflows to make incremental improvements once the root cause of potential problems or anomalies to isolate and address issues promptly.
In addition, DevOps teams can more easily use Intelligent Canaries to monitor the impact changes made are having on the IT environment.
It’s not clear to what degree DevOps teams have embraced observability platforms. In theory, observability has always been a core DevOps principle, but most IT teams rely on monitoring tools that enable them to track a set of pre-defined metrics. Those metrics, however, don’t provide access to log data that DevOps teams can query to determine the root cause of an issue.
The challenge has been that collecting log data requires IT teams to first instrument applications to collect data they can query. Even once that data is collected, they then need to have the expertise required to craft the types of queries that might surface the root cause of an issue. That latter task is becoming simpler with the advent of machine learning algorithms that are capable of suggesting which queries to run, but it still requires a fair amount of DevOps expertise to get the full value of observability.
Cloud Canaries is making a case for an alternative approach to the leverage agent that DevOps teams can easily deploy to pinpoint the root cause of an issue, that previously might have taken days or weeks to ascertain.
Each organization should determine the best observability path forward that makes the most sense for them. However, as application environments become more complex the need for observability is becoming a more pressing issue. Many application environments can no longer be effectively managed without the aid of advanced technologies such as neural networks, machine learning algorithms and other data science techniques. The issue now is how to achieve observability at a time, when thanks to other advances in artificial intelligence (AI), the number of applications running in production environments will soon exponentially increase.
