Instead deploy and configure Azure Cache for Redis. Update the web applications.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/best-practices/caching#managing-concurrency-in-a-cache

Explanation:


Box 1: CreateJob
Box 2: TaskSuccess
TaskSuccess: Upload the file(s) only after the task process exits with an exit code of 0.
Incorrect: TaskCompletion: Upload the file(s) after the task process exits, no matter what the exit code was.
Box 3: TaskFailure
TaskFailure:Upload the file(s) only after the task process exits with a nonzero exit code.
Box 4: OutputFiles
To specify output files for a task, create a collection of OutputFile objects and assign it to the CloudTask.
OutputFiles property when you create the task.
References:
https://docs.microsoft.com/en-us/dotnet/api/microsoft.azure.batch.protocol.models.outputfileuploadcondition
https://docs.microsoft.com/en-us/azure/batch/batch-task-output-files

Explanation:

Box 1: If WEBSITES_ENABLE_APP_SERVICE_STORAGE
If WEBSITES_ENABLE_APP_SERVICE_STORAGE setting is unspecified or set to true, the /home/ directory will be shared across scale instances, and files written will persist across restarts Box 2: /home Reference:
https://docs.microsoft.com/en-us/azure/app-service/containers/app-service-linux-faq

Explanation:

B: The allkeys-lru policy evict keys by trying to remove the less recently used (LRU) keys first, in order to make space for the new data added. Use the allkeys-lru policy when you expect a power-law distribution in the popularity of your requests, that is, you expect that a subset of elements will be accessed far more often than the rest.
C: volatile-lru: evict keys by trying to remove the less recently used (LRU) keys first, but only among keys that have an expire set, in order to make space for the new data added.
Note: The allkeys-lru policy is more memory efficient since there is no need to set an expire for the key to be evicted under memory pressure.
Reference:
https://redis.io/topics/lru-cache