Today we continue discussing the best practice from storage engineering:

245) The process pool per disk worker model has alleviated the need to fork processes and tear down and every process in the pool is capable of executing any of the read-writes from any of the clients. The process pool size is generally finite if not fixed. This has all of the advantages from the process per disk worker model above and with the possibility of differentiated processes in the pool and their quota.
246) When compute and storage are consolidated, they have to be treated as commodity and the scalability is achieved only with the help of scale-out. On the other hand, they are inherently different. Therefore, nodes dedicated to computation may be separated from nodes dedicated to storage. This lets them both scale and load balance independently.
247) Range-based partitioning /indexing is much more beneficial for sequential access such as with stream which makes enumeration easier and faster because of the locality of a set of ranges. This helps with performance. Hash based indexing is better when we have to fan out the processing in their own partitions for performance and all the hashes fall in the same bucket. This helps with load balancing.
248) Third, throttling or isolation is very useful when accounts are not well behaved.  The statistics is collected by the partition server which keeps track of request rates for accounts and partitions.  The same request rate may also be used for load balancing.
249) Automatic load balancing can now be built on range based partitioning approach and the account-based throttling.  This improves multi-tenancy in the environment as well as handling of peaks in traffic patterns.
250) The algorithm for load-balancing can even be adaptive based on choosing appropriate metrics to determine traffic patterns that are well-known.  We start with a single number to quantify load on each partition and each server and then use the product of request latency and request rate to represent loads.