Prefetching and clustering techniques for network based storage.

The usage of network-based applications is increasing, as network speeds increase, and the use of streaming applications, e.g BBC iPlayer, YouTube etc., running over network infrastructure is becoming commonplace. These
applications access data sequentially. However, as processor speeds and the amount of memory available increase, the rate at which streaming applications access data is now faster than the rate at which the blocks can be
fetched consecutively from network storage. In addition to sequential access, the system also needs to promptly satisfy demand misses in order for applications to continue their execution.
This thesis proposes a design to provide Quality-Of-Service (QoS) for streaming applications (sequential accesses) and demand misses, such that, streaming applications can run without jitter (once they are started) and demand misses can be satisfied in reasonable time using network storage. To implement the proposed design in real time, the thesis presents an analytical
model to estimate the average time taken to service a demand miss.
Further, it defines and explores the operational space where the proposed QoS could be provided. Using database techniques, this region is then encapsulated into an autonomous algorithm which is verified using simulation.
Finally, a prototype Experimental File System (EFS) is designed and implemented to test the algorithm on a real test-bed.

A thesis submitted to Middlesex University in partial fullment of the requirements for the degree of Doctor of Philosophy.