App
devices for observation
To achieve
operational excellence, the productive workload must provide the information
necessary to support it. This released information is used to define Service
Line Indicators (SLIs) related to reliability, safety, performance, etc.
Unexpected behavior of user-facing systems or storage systems must be corrected
in time to meet agreed service line objectives (SLOs). A system that cannot
provide enough information to determine its health and behavior is considered
undetectable and therefore difficult for an operator to support.
Hardware
application as a solution
Observability
is the practice of hardware systems with tools for collecting actionable data.
This not only helps monitor and detect symptoms, but also helps companies
understand the root causes of any potential problems. The hardware enables the
system to provide insight into its overall state that is based on telemetry.
Telemetry
consists of three main categories: traces, metrics, and logs, which are
collected at runtime around different cross-interests of the system. These
overarching concerns are aspects of the system that are identified during
system design and managed at runtime with the help of a hardware agent such as
Tomcat or GlassFish servers. To enable or improve the observability of a system,
all components of the application, not just critical services, must be equipped
with observability into account, to tell the whole story.
Hardware
Aspect Oriented Programming (AOP)
Aspect
Oriented Programming (AOP) based frameworks are used to generate and weave
devices with application code. The application developer focuses on developing
the application code and leaves the responsibility of preparing the code to
look and feel the code. The side icon is generated from tips selected at the
breakpoints between intersecting interests. Aspect Weaver does the magic of
connecting aspect code with application code while compiling the code. This
modular approach provided by AOP allows for a clean isolation between
application code and skin code. In addition, it helps with surface code reuse,
simplifies maintenance, and provides much-needed information about code
execution.
When a
system can outsource its health information, monitoring the system can help
better understand and predict when the system will crash and the main reasons
for its failure. A reasonable alert mechanism based on the state of the system
can help human intervention in a timely manner to identify the actual problem
at hand and take action to mitigate the problem. With hardware woven into application
code, it becomes possible to imagine white box monitoring of a system's guts
check. It also helps you focus on the causes rather than just the symptoms. The
telemetry data collected can be used to aid effective debugging to troubleshoot
impending problems. With full monitoring and white box monitoring, the program
team can deliver high-quality programs at high speed, see performance in real
time, and create a culture of innovation.
Hardware
optimization
Hardware
implementation is a non-functional condition and incurs additional
implementation costs, which also depend on the level of hardware required and
the degree of automation that can be achieved with respect to the hardware
required.
Each
programming language allows you to write registers with different logging
levels. Typically, the overhead of writing these records is low. Brendan
Gregg's method of use suggests that companies should use resources to record
meaningful data about key factors such as usage, saturation, and error
counting. Usage can be understood as “the average time a resource has been busy
performing service work,” and saturation can be described as “the degree to
which a resource has additional work that it cannot serve and is often queued.”
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