Navigate hybrid cloud observability with 3 techniques
Every technological change brings benefits and risks, and hybrid cloud is no exception. Businesses have gained performance and productivity by combining public cloud technology with their legacy data center applications.
But to create this symbiotic mixture, they had to increase the complexity of their applications. With this increase, they face a growing problem in identifying and solving problems, as well as the importance of telemetry actionable information. As a result, observability has exploded. How to meet this challenge?
Monitoring vs observability
Monitoring is the traditional method of collecting information on application health and performance, and it works well for monolithic applications and simple datacenter hosting.
Public cloud platforms also offer monitoring services, and many companies adopting hybrid cloud believe that these two sources of monitoring are enough to solve the problems. Almost all find that they are not, because the data from all the places where the applications are running is difficult to interpret.
The difference between monitoring and observability is that the latter is inherently a “holistic” application. Observability provides a view of the state of an application, regardless of how many components it is made of or where those components are hosted. Observability assumes that an application is an ecosystem which must be understood as a cooperative system of elements. The status of the system is derived from the combined status of all of its elements and workflows.
What is hybrid cloud observability?
The essential element of hybrid cloud observability is workflows, for two reasons:
First, workflows define how components and hosting points combine to create the application ecosystem. This ecosystem is made up of the relationships between the elements that allow IT professionals to interpret individual monitoring results.
Second, workflows represent the contribution to network connectivity to the application, and this contribution is vital in hybrid cloud computing. The network is the critical – and sometimes hidden – element of observability in the hybrid cloud. The most successful observability strategies will focus on the network.
Companies that are successful in managing hybrid cloud observability do so primarily through three techniques:
- network traffic analysis;
- automatic tracing to track workflows and identify integrated application components; and
- application performance monitoring (APM) and code modification to introduce context triggers.
Network traffic analysis. For hybrid cloud observability, network traffic analysis is based on a familiar idea: Diagnosing application problems is usually based on problem isolation.
Because network connections link application components in the cloud To associate components in the data center, it is possible to monitor cloud-to-data center traffic and identify specific application workflows. These flows can then isolate a problem in a specific cloud or data center, and from there, traditional monitoring can isolate the problem. The challenge here is identifying the traffic at the data rates involved in hybrid cloud connections.
Automatic tracing. Tracing is the general approach to hybrid cloud observability because it does not depend on access to source code. It also focuses on the workflows that link components and the network connectivity that supports them, ensuring that network behavior is fed into application monitoring.
However, the automatic tracing process is not a fully positive mechanism for workflow and component identification. And this can be problematic when there is significant reuse of components; in the public cloud, where a large number of web services are used; and where the traffic flows and component connections are masked by the cloud provider.
Code modification and APM. These actions allow companies to generate tracing signals at specific points, and a strong autotrace strategy incorporates at least some technologies for code changes – usually open source options – to improve observability. Because the inserted code identifies the specific set of developer’s observability conditions, APM provides the most precise approach to improve observability in a hybrid cloud model.
The downside to APM is that not all tools support all programming languages, and source code is not available for all components of the application.
Many enterprise hybrid cloud applications involve new developments in the cloud, related to legacy data center components. The boundary point between the two may be a small “wedge” layer – which connects application interfaces to modules – data center code. Since developers can easily introduce code changes into any new development project, a cloud border wedging layer makes it easier to interpret cloud and data center monitoring data by providing a contextual link between the two, that was created by the trace event generated by the code change. .
The best approach for hybrid cloud observability is often a combination of the three strategies identified here. APM and code modification approaches will offer the best approach where they can be applied, and automatic tracing and analysis of network traffic will fill any critical gaps.
It is important to remember that for most companies, no observability strategy will be 100% efficient and, in most cases, it is possible to push attempts too far at full observability, increasing costs without corresponding gain in terms of benefits. Once the majority of problems can be reliably identified and resolved, it is best to measure other changes in cost / benefit terms to avoid undue operational and financial impacts. More observability is not always better.