A Cost-Effective and Multi-Source-Aware Replica Migration Approach for Geo-Distributed Data Centers
Bibliographic record
Abstract
Geographically distributed data centers have been de-ployed for different purposes, such as minimizing the transmission and response time and the amount of data exchanges throughout the networks. Beyond fault tolerance purposes, data replication has been a popular solution to increase data availability by bringing data closer to the end users. Most existing studies migrate replicas to the desired destinations from a single source, and few of such solutions are cost-aware. By having a multi-source and cost-aware approach, we can accelerate the transmission time resulting in a better quality of service for the end users. Towards that end, this paper introduces a cost-effective and deadline-aware replica migration approach for geo-distributed data centers. The proposed model discovers the appropriate source(s) and paths to transmit the replicas to a desired destination cost-effectively. This problem, which jointly considers cost and deadline, is formulated into a mixed-integer linear programming optimization model. Extensive evaluation against two of the most recent approaches shows significant improvement in meeting the deadlines and reducing the cost incurred to the customers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".