We continue to read up on Riverbed Optimization System (RiOS) technical overview. In the previous post, we discussed RiOS as a transparent TCP proxy where an innovative three channel mechanism replaces the logical single end to end connection. With RiOS, all TCP traffic can be intercepted and accelerated regardless of the application that generates it. This means that there is no application category or bracket to which RiOS is specific. In addition, transport streamlining optimizes the behavior of TCP on the WAN. And these can be applied to both encrypted data and clear.
Application caching is therefore not required which anyways is application specific and providing marginal benefits.
That mentioned we can refer to application specific optimizations. There are some applications like Windows File sharing or Exchange e-mail, where the application protocol matters. Aside from data and transport streamlining, application streamlining enables RiOS to alleviate application specific behavior although introducing TCP optimizers or data compression devices have shown limited performance gains.
Having discussed traffic, we now shift attention to data and disk usages. RiOS implements a Universal Data Store. This lets it reduce data to efficiently scale across multiple peers because of the use of a single store. The significance of this improvement can be seen in the number of per-peer data store savings. Moreover per peer segmentation often reduces usable disk space to very small portions. This manifests in such performance bottlenecks as data store "misses" or "cold hits". On the other hand, with a universal datastore and accelerated traffic, RiOS can now support efficient sharing in a large scale enterprise environment.
The data is guaranteed to be coherent because the single copy is accessed through the server. The server handles permissions and file locking. The client requests happen as if the intermediary accelerator device is not there. Hence, the data can be considered original by branch offices. It may be interesting to note that in the absence of the single copy of server, obsolete and multiple stale versions of the master have a chance to proliferate. Perhaps its worthwhile to mention that the locking available from the server also includes OpLocks which enables latency optimizations over CIFS that can help reduce contention for the same file.
#codingexercise answer
In response to the question asked in the previous post, something like this could work :
var l = new SortedList ();
Items.ForEach ( x => l.Add (x) ),
Application caching is therefore not required which anyways is application specific and providing marginal benefits.
That mentioned we can refer to application specific optimizations. There are some applications like Windows File sharing or Exchange e-mail, where the application protocol matters. Aside from data and transport streamlining, application streamlining enables RiOS to alleviate application specific behavior although introducing TCP optimizers or data compression devices have shown limited performance gains.
Having discussed traffic, we now shift attention to data and disk usages. RiOS implements a Universal Data Store. This lets it reduce data to efficiently scale across multiple peers because of the use of a single store. The significance of this improvement can be seen in the number of per-peer data store savings. Moreover per peer segmentation often reduces usable disk space to very small portions. This manifests in such performance bottlenecks as data store "misses" or "cold hits". On the other hand, with a universal datastore and accelerated traffic, RiOS can now support efficient sharing in a large scale enterprise environment.
The data is guaranteed to be coherent because the single copy is accessed through the server. The server handles permissions and file locking. The client requests happen as if the intermediary accelerator device is not there. Hence, the data can be considered original by branch offices. It may be interesting to note that in the absence of the single copy of server, obsolete and multiple stale versions of the master have a chance to proliferate. Perhaps its worthwhile to mention that the locking available from the server also includes OpLocks which enables latency optimizations over CIFS that can help reduce contention for the same file.
#codingexercise answer
In response to the question asked in the previous post, something like this could work :
var l = new SortedList ();
Items.ForEach ( x => l.Add (x) ),
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