Comparing the performance of web server architectures
Bibliographic record
Abstract
In this paper, we extensively tune and then compare the performance of web servers based on three different server architectures. The μserver utilizes an event-driven architecture, Knot uses the highly-efficient Capriccio thread library to implement a thread-per-connection model, and WatPipe uses a hybrid of events and threads to implement a pipeline-based server that is similar in spirit to a staged event-driven architecture (SEDA) server like Haboob. We describe modifications made to the Capriccio thread library to use Linux's zero-copy sendfile interface. We then introduce the SY mmetric Multi-Processor Event Driven (SYMPED) architecture in which relatively minor modifications are made to a single process event-driven (SPED) server (the μserver) to allow it to continue processing requests in the presence of blocking due to disk accesses. Finally, we describe our C++ implementation of WatPipe, which although utilizing a pipeline-based architecture, excludes the dynamic controls over event queues and thread pools used in SEDA. When comparing the performance of these three server architectures on the workload used in our study, we arrive at different conclusions than previous studies. In spite of recent improvements to threading libraries and our further improvements to Capriccio and Knot, both the event-based μserver and pipeline-based Wat-Pipe server provide better throughput (by about 18%). We also observe that when using blocking sockets to send data to clients, the performance obtained with some architectures is quite good and in one case is noticeably better than when using non-blocking sockets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".