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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".