Improving Resource Utilization of Enterprise-Level World-Wide Web Proxy Servers
Carlos Maltzahn, Improving Resource Utilization of
Enterprise-Level World-Wide Web Proxy Servers, PhD Thesis, Submitted to
the Graduate School. Boulder, Colorado: University of Colorado at
Boulder, April, 1999
Abstract
The resource utilization of enterprise-level Web
proxy servers is primarily dependent on network and disk I/O latencies
and is highly variable due to a diurnal workload pattern with very
predictable peak and off-peak periods. Often, the cost of resources
depends on the purchased resource capacity instead of the actual
utilization. This motivates the use of off-peak periods to perform
speculative work in the hope that this work will later reduce resource
utilization during peak periods. We take two approaches to improve
resource utilization.
In the first approach we reduce disk I/O by cache compaction during off-peak periods and by carefully designing the way a cache architecture utilizes operating system services such as the file system buffer cache and the virtual memory system. Evaluating our designs with workload generators on standard file systems we achieve disk I/O savings of over 70% compared to existing Web proxy server architectures.
In the second approach we reduce peak bandwidth levels by prefetching bandwidth during off-peak periods. Our analysis reveals that 40% of the cacheable miss bandwidth is prefetchable. We found that 99% of this prefetchable bandwidth is based on objects that the Web proxy server under study has not accessed before. However, these objects originate from servers which the Web proxy server under study has accessed before. Using machine learning techniques we are able to automatically generate prefetch strategies of high accuracy and medium coverage. A test of these prefetch strategies on real workloads achieves a peak-level reduction of up to 12%.
In the first approach we reduce disk I/O by cache compaction during off-peak periods and by carefully designing the way a cache architecture utilizes operating system services such as the file system buffer cache and the virtual memory system. Evaluating our designs with workload generators on standard file systems we achieve disk I/O savings of over 70% compared to existing Web proxy server architectures.
In the second approach we reduce peak bandwidth levels by prefetching bandwidth during off-peak periods. Our analysis reveals that 40% of the cacheable miss bandwidth is prefetchable. We found that 99% of this prefetchable bandwidth is based on objects that the Web proxy server under study has not accessed before. However, these objects originate from servers which the Web proxy server under study has accessed before. Using machine learning techniques we are able to automatically generate prefetch strategies of high accuracy and medium coverage. A test of these prefetch strategies on real workloads achieves a peak-level reduction of up to 12%.
