The DOI matches its Crossref record.
SEDA: an architecture for well-conditioned, scalable internet services
Abstract
We propose a new design for highly concurrent Internet services, which we call the staged event-driven architecture (SEDA). SEDA is intended to support massive concurrency demands and simplify the construction of well-conditioned services. In SEDA, applications consist of a network of event-driven stages connected by explicit queues. This architecture allows services to be well-conditioned to load, preventing resources from being overcommitted when demand exceeds service capacity. SEDA makes use of a set of dynamic resource controllers to keep stages within their operating regime despite large fluctuations in load. We describe several control mechanisms for automatic tuning and load conditioning, including thread pool sizing, event batching, and adaptive load shedding. We present the SEDA design and an implementation of an Internet services platform based on this architecture. We evaluate the use of SEDA through two applications: a high-performance HTTP server and a packet router for the Gnutella peer-to-peer file sharing network. These results show that SEDA applications exhibit higher performance than traditional service designs, and are robust to huge variations in load.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
(2007) · doi:10.1145/1294261.1294281 · Cited by 3,447
A catalog of stream processing optimizations
Hirzel, M., Soulé, R., Schneider, S., Gedik, B., & Grimm, R. (2014) · ACM Computing Surveys · doi:10.1145/2528412 · Cited by 299
AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization
Chen, L., Lingys, J., Chen, K., & Liu, F. (2018) · Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication · doi:10.1145/3230543.3230551 · Cited by 282
TensorFlow-Serving: Flexible, High-Performance ML Serving
Olston, C., Fiedel, N., Gorovoy, K., Harmsen, J., Lao, L., Li, F., Rajashekhar, V., Ramesh, S., & Soyke, J. (2017) · arXiv · doi:10.48550/arxiv.1712.06139 · Cited by 171
Real-time big data processing framework: Challenges and solutions
(2015) · Applied Mathematics & Information Sciences · doi:10.12785/amis/090646 · Cited by 74
Metadata
- Authors
- Welsh, M., Culler, D., & Brewer, E.
- Venue
- Proceedings of the eighteenth ACM symposium on Operating systems principles
- Publisher
- ACM
- Published
- 2001-10-21
- Pages
- 230-243
- OpenAlex
- W2126022651
- Cited by
- 834
- References
- 0
- FWCI
- 21.68
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: SEDA: an architecture for well-conditioned, scalable internet services. https://app.cochat.ai/papers/10.1145/502034.502057
- The paper
- Welsh, M., Culler, D., & Brewer, E. (2001). SEDA: an architecture for well-conditioned, scalable internet services. Proceedings of the eighteenth ACM symposium on Operating systems principles. https://doi.org/10.1145/502034.502057
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.