Prison Break: A Generic Schema Matching Solution to the Cloud Vendor Lock-in Problem
Bibliographic record
Abstract
Porting applications from one cloud platform to another is difficult, making vendor lock-in a major impediment to cloud adoption. Model-driven engineering could be used to determine how applications might run on different platforms, if platform schemas could be matched. However, schema matching typically relies on linguistic and structural similarities, and cloud schema terms diverge so much that such matching is impossible. To address this challenge, we introduce Prison Break: a novel, semi-automated and generic schema matching process. Prison Break solves the divergent vocabulary problem by using web search results as a similarity metric, thus incorporating domain knowledge without constructing a dictionary, lexicon or thesaurus. We tested Prison Break by matching schemas from two major cloud providers: Windows Azure and Google Application Engine. We determined that Prison Break helps solve the vendor lock-in problem by reducing the manual efforts required to map complex correspondences between cloud schemas. This brings us one step closer to automatic model migration across cloud platforms.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".