This thesis evaluates the improved use of data compression to reduce storage space, increase throughput and reduce bandwidth requirements. The latter is an interesting field of application, not only for telecommunication but also for local data transfer between the CPU and the storage device. The choice of the compression algorithm is crucial for the overall performance. For this reason part of this work is the reflection of which algorithm fits best to a particular situation. The goal of this thesis comprises the implementation of three different features. At first, updating the existing lz4 algorithm enables support for the “acceleration” called lz4fast. This compression speed-up, in lieu of compression ratio, increases write speed on fast storage devices such as SSDs. Second, an automatic decision procedure adapts the compression algorithms gzip-(1-9) and the new updated lz4 to the current environment in order to maximize utilization of the CPU and the storage device. Performance is improved compared to no compression but is highly depends on the hardware setup. On powerful hardware the algorithm successfully adapts to the optimum. The third and last feature enables the user to select a desired file-write-throughput. Scheduling is implemented by delaying and prioritizing incoming requests. Thereby compression is adjusted to not impair the selected requirements while reducing storage space and reducing bandwidth demand respectively. By preferring “fast” files over “slow” files - high throughput over low throughput - the average turnaround time is reduced while maintaining the average compression ratio.

@mastersthesis{QOSIIZTCB17,
	author	 = {Niklas Bunge},
	title	 = {{Quality of service improvement in ZFS through compression}},
	advisors	 = {Michael Kuhn and Anna Fuchs},
	year	 = {2017},
	month	 = {05},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{https://wr.informatik.uni-hamburg.de/_media/research:theses:niklas_bunge_quality_of_service_improvement_in_zfs_through_compression.pdf}}},
	type	 = {Master's Thesis},
	abstract	 = {This thesis evaluates the improved use of data compression to reduce storage space, increase throughput and reduce bandwidth requirements. The latter is an interesting field of application, not only for telecommunication but also for local data transfer between the CPU and the storage device. The choice of the compression algorithm is crucial for the overall performance. For this reason part of this work is the reflection of which algorithm fits best to a particular situation. The goal of this thesis comprises the implementation of three different features. At first, updating the existing lz4 algorithm enables support for the "acceleration" called lz4fast. This compression speed-up, in lieu of compression ratio, increases write speed on fast storage devices such as SSDs. Second, an automatic decision procedure adapts the compression algorithms gzip-(1-9) and the new updated lz4 to the current environment in order to maximize utilization of the CPU and the storage device. Performance is improved compared to no compression but is highly depends on the hardware setup. On powerful hardware the algorithm successfully adapts to the optimum. The third and last feature enables the user to select a desired file-write-throughput. Scheduling is implemented by delaying and prioritizing incoming requests. Thereby compression is adjusted to not impair the selected requirements while reducing storage space and reducing bandwidth demand respectively. By preferring "fast" files over "slow" files - high throughput over low throughput - the average turnaround time is reduced while maintaining the average compression ratio.},
}