The ongoing trend among enterprises is the migration of systems to cloud-hosted platforms. Nearly 90% of respondents to a survey of CIOs stated that their company had some plan or strategy in place to migrate from on-premises deployments to the cloud. In addition to migrations from on-premises to the cloud, let's not forget enterprises also migrate systems from one cloud platform to another.
Modern applications are complex regardless of the migration path and involve multiple layers. Traditional monitoring methods rely on teams analyzing logs and metrics. However, modern applications' highly distributed and ephemeral nature makes this almost impossible. Engineers would need to collect logs and metrics across architectural components that can number in the hundreds.
Enterprises embarking on an application migration need more advanced methods to fully understand the success and health of their applications—before and after the migration
In this blog, we'll examine how observability can help you ensure the smooth migration of applications to the cloud. We'll explore what observability is, its components, and how it works.
What is Observability?
Observability is the concept of inferring a system's internal health and state based on its outputs. Those outputs—also called "telemetry data"—are logs, metrics, and traces. By analyzing this data with the help of AI, machine learning, and observability technology, the IT team can gain a comprehensive and holistic view of the health of a service to keep it running optimally.
Logs are a timestamped record of events generated for an application or infrastructure component. An engineer can use logs to troubleshoot bugs and gain insight into a system's performance and security. You can even use logs to derive custom metrics. Logs provide a snapshot of the who, what, when, where, and why of an event.
You can use logs as part of more advanced processes. For example, AI for IT Operations (AIOps) uses historic log analytics for anomaly detection. When migrating an application, you can use log data with advanced logging tools to compare operational functionality between the legacy state and the new state. However, log files accumulate and multiply. For robust logging of scalable systems, many enterprises connect their log generation with an observability platform that manages logs for storage, query, and analysis.
A metric is a quantifiable measure of an infrastructure or application component characteristic. Metrics can be real-time values, averages, aggregated values, or summaries. Engineers use metrics to inform about operational efficiency and the reliability of a system. By collecting metrics, you can better visualize problems, system trends, and anomalies.
For example, some metrics indicate a lull in system demand during certain times of the year, which engineers can use to better plan resource allocation. Metrics can indicate a surge of unusual requests from a system user, alerting teams to a security threat
When migrating systems to cloud applications, you should collect performance benchmarks for the legacy state. By including that data in an observable system, you can use it to compare and contrast the new state for areas of improvement.
Traces are pieces of information all related to a single user transaction but collected throughout the journey of that transaction across the components of a service. Traces return important contextual values regarding an application's ability to execute its functions. Examples of that context data include
- Database queries performed
- Functions called
- Requests in a service
To collect traces, you need code instrumentation. Code instrumentation refers to making changes at the code level to monitor and evaluate the performance of your code execution. Tracers comprise spans that attach to requests as they move through the system, branching into child spans as they trace the request path from end to end.
Unlike logs and metrics that also collect data throughout the infrastructure, traces are collected from the application. You can implement tracing manually or through the use of automated tools. Among the several open-source tracing frameworks available, OpenTelemetry is a set of APIs and SDKs that is widely used.
During application migration, traces play a pivotal role in providing a comprehensive understanding of the entire end-to-end path of request execution. While refactoring legacy applications, this contextual information helps you debug and troubleshoot code issues early.
Observability is an evolution of APM
Logs, metrics, and traces are vital components fueling APM. However, the distributed and increasingly complex nature of modern applications require more sophisticated monitoring methods. Observability leverages APM but adds further extensibility and automation, giving teams a more complete visibility of a service.
Cloud Application Migration and the Need for Observability
Imagine a scenario in which an enterprise migrates from an on-premises to a cloud-based environment. The following table compares the on-premises layers with the cloud layers:
The layers outlined above also apply to other cloud configurations, like hybrid or multi-cloud migrations.
When comparing the two, it's important to note the following:
Modern applications typically use a microservices-based architecture, meaning they tend to be far more distributed and layered, as each service in the application acts as an individual component.
- Unless you are doing a lift-and-shift migration, mapping these components is seldom one-to-one.
- The newly migrated application still needs to perform better than the legacy application.
When addressing these points, you can use benchmarking to analyze the cloud application and the legacy application. However, traditional monitoring techniques used for your legacy systems can't provide a complete picture of your application once it's hosted in a cloud-based environment.
Engineers can't manually collect and analyze logs from hundreds of individual components. They can't rely on metrics to explain blockages in loosely coupled infrastructure or traces to detect failed cluster nodes.
To understand the performance and state of a new cloud migration, you need to implement observability in each layer of every component. Then you can feed those outputs into a centralized system for correlation and analysis.
How to Use Observability for Smooth Cloud Application Migration
The observability data you need will depend on your architecture, its complexities, and the criticality of the monitoring. Although it isn't an exhaustive list, here are some common cases:
Networking logs within your Virtual Private Cloud can help you identify and analyze suspicious or unwanted traffic. Firewall logs are also useful for incident response handling or simply for debugging purposes. For example, these logs can inform which ports or IPs are relevant for the application.
You can begin with established benchmarks for common server metrics in your legacy application, including:
- Disk space
- I/O latency
By capturing these same metrics in the new cloud environment, you can compare to confirm the migrated application is performant. You can also discover and address any errors or missing dependencies in the migrated application.
Web server logs help you analyze errors, such as missing headers or bad status codes and dependencies. These can be used in comparison with the legacy application and for patching the source code to suit the cloud environment, if needed.
Database logs and metrics are important when you're debugging queries that fail or take a suspiciously long time to execute. You can compare this data against the legacy application to match or supersede established performance benchmarks.
Real user monitoring
It's vital to understand if (and how) users are being affected by the application migration. Real user monitoring (RUM) provides insight into the impact of the migration on the UI. Inputting RUM data into your observable system can help gauge the post-migration experience for your users.
Cloud components, such as functions and container orchestration, can generate data that give insight into resource provision and application error debugging. This can aid with resource provisioning and code changes if necessary.
For example, serverless functions can show if an application is running as expected, while container runtime logs can show container OS or application error messages. Container orchestration logs can indicate if too many containers are spinning up, pointing you to resource bottlenecks or application crashes. Meanwhile, message queue metrics might indicate possible bottlenecks in the message sink.
By enabling heavy tracing in the migrated cloud applications, you can zero in on how each request traverses through the system. This gives developers maximum visibility of how an application performs in the new environment, along with any adjustments they might need to make in the application.
For further reading, this article provides an excellent example of how an APM can help during a migration.
How Modern Observability Platforms Work With Cloud Applications?
Modern observability platforms include many automated features to ensure smooth data collection and analysis. They use standards like OpenTelemetry, to gather data. They interface with cloud-based providers, infrastructure, and various applications. They support integration with commonly used open-source tools such as Prometheus, Grafana, and Loki, leveraging native integrations and agents.
Observability platforms make use of AI/ML to provide predictive analysis and automated troubleshooting. When consolidated in a single platform, all this data and automation give stakeholders the visibility they need for a smooth migration to cloud platforms.
As today's enterprises require scalability, reliability, and flexibility for their services, migrating to cloud environments is increasingly common. A smooth service migration is critical, to ensure a seamless user experience. Observability solutions can help bring that assurance by providing complete visibility of your system internals.
With a robust observability platform, you can detect problems that arise during a migration effort, even identifying the root causes of unknown unknowns—the issues that you weren't anticipating. Observability feeds data into AI/ML tools and connects them with self-healing mechanisms, thereby predicting potential incidents and fixing them before they affect your application.
SolarWinds Observability gives you a 360-degree view into these aspects of your system through a single pane of glass. It comes with advanced tools and uses visual dashboards to help you set baselines and monitor your system's state pre and post-migration.
Just as cloud migrations are inevitable for forward-looking enterprises, adopting observability practices and platforms is essential for successful cloud migrations. If you're ready to learn more start a trial of SolarWinds Observability today. If you've done a migration let us know how it went.