Demo Data from VLDB 2026 / reviewers in the wild / expert
Michael Courage
dblp:32/181
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
benchmarking |
0.0 | 1 | 1998 | A Self-Scaling and Self-Configuring Benchmark for Web Servers (Extended Abstract) · SIGMETRICS 1998 |
Performance modeling and evaluation › benchmarking
web server benchmarking |
0.0 | 1 | 1998 | A Self-Scaling and Self-Configuring Benchmark for Web Servers (Extended Abstract) · SIGMETRICS 1998 |
Performance modeling and evaluation › workload characterization › commercial workloads
web server workload |
0.0 | 1 | 1998 | A Self-Scaling and Self-Configuring Benchmark for Web Servers (Extended Abstract) · SIGMETRICS 1998 |
Performance modeling and evaluation
workload characterization |
0.0 | 1 | 1998 | A Self-Scaling and Self-Configuring Benchmark for Web Servers (Extended Abstract) · SIGMETRICS 1998 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | A Self-Scaling and Self-Configuring Benchmark for Web Servers (Extended Abstract)abstractWorld Wide Web clients and servers have become some of the most important applications in our computing base, and we need realistic and meaningful ways of measuring their performance. Current server benchmarks do not capture the wide variation that we see in servers and are not accurate in their characterization of web traffic. In this paper, we present a self-configuring, scalable benchmark that generates a server benchmark load based on actual server loads. In contrast to other web benchmarks, our benchmark focuses on request latency instead of focusing exclusively on throughput sensitive metrics. We present our new benchmark hBench:Web, and demonstrate how it accurately models the load of an actual server. The benchmark can also be used to assess how continued growth or changes in the workload will affect future performance. Using existing log histories, we now that these predictions are sufficiently realistic to provide insight into tomorrow's Web performance. Stephen Manley, Margo I. Seltzer, Michael Courage |
SIGMETRICS | 3 |