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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".