Author manuscript, published in "International Conference on Automated Software Engineering, Montréal: Canada (2003)" A Programmable Client-Server Model: Robust Extensibility via DSLs
Bibliographic record
Abstract
The client-server model has been successfully used to support a wide variety of families of services in the context of distributed systems. However, its server-centric nature makes it insensitive to fast changing client characteristics like terminal capabilities, network features, user preferences and evolving needs. To overcome this key limitation, we present an approach to enabling a server to adapt to different clients by making it programmable. A service-description language is used to program server adaptations. This language is designed as a domain-specific language to offer expressiveness and conciseness without compromising safety and security. We show that our approach makes servers adaptable without requiring the deployment of new protocols or server implementations. We illustrate our approach with the Internet Message Access Protocol (IMAP). An IMAP server is made programmable and a language, named Pems, is introduced to program robust variations of e-mail services. Our approach is uniformly used to develop a platform for multimedia communication services. This platform is composed of programmable servers for telephony services, e-mail processing, remote-document processing and stream adapters. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.312 | 0.093 |
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".