Compiling Distributed System Models with DCal [evaluation]
Bibliographic record
Abstract
Distributed systems are difficult to design and implement correctly. In response, both research and industry are exploring applications of formal methods to distributed systems. A key challenge in this domain is the missing link between the formal design of a system and its implementation. Today, practitioners bridge this link through manual effort. We present a language called Modular PlusCal that extends PlusCal by cleanly separating the model of a system from a model of its environment. We then present a compiler tool-chain called DCal that automatically translates MPCal models to TLA+ for model checking, and that also compiles MPCal models to runnable Go code. DCal provides system designers with a new ability to model and check their designs, and then re-use their modeling efforts to mechanically extract runnable implementations of their designs. Our evaluation shows that the DCal approach works for complex models: we model check, compile, and evaluate the performance of MPCal systems based on Raft and CRDTs. Compared to previous work, DCal requires less time to develop a checked model and derive a fully working implementation. With DCal we created a formally checked Raft model and its corresponding implementation in under 1 person-month, which is 3×3\times3× less than Ivy. Our evaluation shows that a DCal-based Raft KV store with three nodes has 41% higher throughput than a Raft KV store based on Ivy, the highest performing verified Raft-based KV store from related work. A DCal-based CRDT set has a latency within 2×2 \times2× of a CRDT set implementation from SoundCloud called Roshi.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".