Online cost minimization for operating geo-distributed cloud CDNs
Bibliographic record
Abstract
Cloud-based content delivery networks (Cloud CDN) cache and deliver contents from geo-distributed cloud data centers to end users across the globe, exploiting "infinite" on-demand cloud resources to address volatile user demands. It is critically important to efficiently manage cloud resources in different locations over time, for minimization of the operational cost of the CDN provider, while delivering short response delay to user requests. Although many have studied cost-aware replica placement and request redirection in CDN systems, most are restricted to an offline or one-time setting, or resort to greedy heuristics for online operation. This work proposes an efficient online algorithm for dynamic content replication and request dispatching in cloud CDNs operating over a long time span, targeting overall cost minimization with performance guarantees. Our online algorithm consists of two main modules: (1) a regularization method from the online learning literature to convert the offline cost-minimization optimization problem into a sequence of regularized problems, each to be efficiently solvable in one time slot; (2) a randomized approach to convert the optimal fractional solutions from the regularized problems to integer solutions of the original problem, achieving a good competitive ratio. The effectiveness of our online algorithm is validated through solid theoretical analysis and trace-driven simulations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".