Bibliographic record
Abstract
This dissertation presents a novel approach to enable tunable tradeoffs between performance and consistency for geographically replicated cloud services.The end goal of the approach is to have a solution by which replication is able to improve the overall performance of the system while inconsistency due to optimistic message delivery is bounded in both time and space.We achieve the latter goal with an eagerly executed commit layer that detects and solves conflicts in parallel with the optimistic replication layer.We believe such a solution not only solves the performance bottleneck of replicated services but also enables a full spectrum of tunability which will further allow applications to choose the best strategy to balance the tradeoff between performance and consistency for their replicated services.The new solution differs from the traditional eventual consistency model by providing a capability to solve conflicts in an online manner and to leverage the explicit role of clients in specifying the consistency requirements.Experiments on a prototype system in a real cloud environment are described, that show that the Client Oriented Layered Optimistic Replication (or COLOR) is feasible, and which evaluate the achievable tradeoffs between performance and consistency on a realistic example system under load, distributed over five replicas in two continents.The prototype shows that COLOR provides a well defined programming model to assist application developers to control the replication of their cloud services without resorting to the otherwise non-guarantee eventual consistency model in face of performance challenges.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".