This is a continuation of the earlier posts starting with this one: http://ravinote.blogspot.com/2020/09/best-practice-from-networking.html
Networking, compute and storage are inter-dependent. We use data structures to keep the information we want to access in a convenient form. When this is persisted, it mitigates faults in the processing. Artifact brings in additional chores and maintenance while it is cheaper to execute the logic. Versioning of logic helps track the changes. When there is a trade-off between computing and storage for numerous small and cheap artifacts, it is better to generate them dynamically.
The above has a far-reaching impact when there are layers involved and the cost incurred in the lower layer bubbles up to the top layer.
Compute tends to be distributed in nature while storage tends to be local. They can be mutually exclusive in this regard.
Compute-oriented processing can scale up or out while storage must scale-out.
Compute oriented processing can assign priority dynamically but storage tends to remain in a class
Background tasks may sometimes need to catch up with the current activities. In order to accommodate the delay, they may either be run upfront so that changes to be processed are incremental or they can increase in number to divide up the work.
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