Server Capacity Planning for Web Traffic Workload

Abstract

ÐThe goal of this paper is to provide a methodology for determining bandwidth requirements for various hardware components of a World Wide Web server. The paper assumes a traditional symmetric multiprocessor (SMP) architecture for the web-server, although the same analysis applies to an SMP node in a cluster. The paper derives formulae for bandwidth demands for memory, processor data bus, network adapters, disk adapters, I/O-memory paths, and I/O buses. Since the web workload characteristics vary widely, three sample workloads are considered for illustrative purposes: 1) standard SPECweb96, 2) a SPECweb96-like workload that assumes dynamic data and retransmissions, and 3) WebProxy, which models a web proxy server that does not do much caching and, thus, has rather severe requirements. The results point to a few general conclusions regarding Web workloads. In particular, reduction in memory/data bus bandwidth by using the virtual interface architecture (VIA) is very desirable, and the connectivity needs may go well beyond the capabilities of traditional systems based on the traditional PCI-bus. Web workloads also demand a significantly higher memory bandwidth than data bus bandwidth and this disparity is expected to increase with the use of VIA. Also, the current efforts to offload TCP/IP processing may require a larger headroom in I/O subsystem bandwidth than in the processor-memory subsystem. Index TermsÐWeb server, traffic characterization, self-similarity, symmetric multiprocessors, caching/proxy server, band-

DOI: 10.1109/69.806933

Extracted Key Phrases

13 Figures and Tables

Statistics

0510'01'03'05'07'09'11'13'15'17
Citations per Year

51 Citations

Semantic Scholar estimates that this publication has 51 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Kant1999ServerCP, title={Server Capacity Planning for Web Traffic Workload}, author={Krishna Kant and Youjip Won}, journal={IEEE Trans. Knowl. Data Eng.}, year={1999}, volume={11}, pages={731-747} }