Unified SLO Driven Observability across AWS and GCP using CloudWatch and Stack driver and ELK and Splunk with Error Budgets
This paper proposes and evaluates a unified SLO-driven observability framework that integrates AWS CloudWatch, GCP Cloud Monitoring, ELK, and Splunk through a provider-independent meta-model and error-budget governance, demonstrating significant improvements in detection time, recovery speed, SLO stability, and operational cost reduction in multi-cloud environments.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the modern digital world, large organizations often run their software across multiple cloud computing platforms at once, such as Amazon Web Services and Google Cloud Platform. This strategy offers flexibility and safety, but it creates a complex problem: how do you know if the entire system is working correctly when it is split between different providers? To answer this, engineers rely on a concept called a Service Level Objective, which is simply a promise about how well a service should perform, such as staying available 99.9 percent of the time. When a system fails to meet this promise, it consumes a limited reserve of acceptable mistakes, known as an error budget. If this budget runs out, the system must stop new changes to prevent further damage. The challenge arises because each cloud provider uses its own unique language and tools to measure performance, making it difficult to see the whole picture or manage that shared budget effectively.
A researcher at the American University, Ehtesham Junaid, set out to solve this fragmentation by building a unified system that treats these different clouds as a single entity. The study focused on creating a central layer that could translate the different monitoring signals from Amazon's CloudWatch and Google's Cloud Monitoring into a common language. This layer also connected to powerful data analysis tools like ELK and Splunk, which are used to sift through massive amounts of logs and performance records. The goal was not just to collect more data, but to create a single, consistent view of reliability that could automatically trigger safety measures when the error budget was being spent too quickly.
To test this idea, the researcher built a controlled environment where identical software applications ran simultaneously on both AWS and Google Cloud. They simulated real-world problems, such as sudden spikes in traffic, network failures, and partial system shutdowns, to see how the system reacted. First, they observed the setup using the traditional method, where each cloud provider's monitoring tools operated independently. Then, they switched on the new unified framework, which normalized all the incoming data and calculated a single, shared error budget for the entire multi-cloud system. This allowed the system to detect issues faster and make decisions based on the health of the whole network rather than just one part.
The results showed a clear advantage for the unified approach. When faults were introduced, the new system detected the problems in an average of 3.1 minutes, a significant improvement over the 7.8 minutes it took with the separate, siloed tools. This 60 percent reduction in detection time meant that engineers could respond to issues much sooner. Furthermore, the time it took to recover and return to normal operation dropped by nearly 40 percent. The unified system also proved more stable, reducing the inconsistencies in how different parts of the system reported their status. While the cost of gathering and processing the data increased slightly due to the centralization, the overall cost of running the system fell by 18 percent. This saving came from a dramatic 36 percent reduction in the losses associated with system downtime, proving that the investment in better visibility paid off by preventing expensive outages.
The study concludes that the difficulty in managing reliability across multiple clouds is not a lack of good tools, but rather a lack of a shared governance structure. By creating a single, provider-independent way to define goals and manage the budget for mistakes, organizations can achieve a level of control that was previously impossible. The research demonstrates that when reliability is managed as a unified concept rather than a collection of separate tasks, systems become faster to repair, more stable, and more economical to operate. This approach offers a practical foundation for companies that need to maintain high standards of performance while navigating the complexities of a multi-cloud future.
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