Network engineering continued...

This is a continuation of the earlier posts starting with this one: http://ravinote.blogspot.com/2020/09/best-practice-from-networking.html

 Application Configuration – Most networking products are best served with a static configuration that can determine the parameters of the system. Since product usages span a wide variety of deployments, most products offer at least a few parameters to tune the system. Configuration files have been used with applications and servers on the Unix flavor platform and networking products also make use of it. Files also make it easy for listeners to watch for changes. 

 Dynamic Configuration – Applications and services have not only used configuration based on static files but also used dynamic configuration which may be obtained from external configuration sources. Since the value is not updated by touching the production server, this finds appeal in cases where parameters need constant tuning and have to be done without involving the production server. 

Platform independent client library – Most frontends for networking products rely on some form of JavaScript for client-side scripting. However, JavaScript is also popular in server-side programming as a NodeJs server. While portals and application servers are portable when written in JavaScript, it applies equally to any client library or agent interaction for the cloud server 

External management tools – For object storage, S3 has been a primary API for control and data path to the storage server. Management tools that are cloud agnostic provide tremendous leverage for bulk automation and datacenter computing. Consequently, storage products must strive for conforming to these tools in ways that utilize already streamlined channels such as well-published APIs whenever possible. 

Statistics – We referred to statistics enabled counters earlier for the components of the storage server. This section merely refers to client-based statistics for the entire storage product whenever possible so that there can be differentiated tuning to workloads based on the data gathered from the server usage.