Fog computing in multi-tier data center networks: A hierarchical game approach
Bibliographic record
Abstract
With the increasing popularity of data services and applications, data center networks have been introduced to serve users in a centralized fashion. Furthermore, the flexibility of the data service subscribers' (DSSs') requirements motivates data center virtualization so as to optimize the resource allocation among all DSSs. However, as the massive data centers are usually far away from the DSSs, the quality of services are severely affected for delay-sensitive DSSs. Accordingly, fog computing is considered to solve the problem, where some virtualized edge data centers acting as fog nodes (FNs) are added in the network and help massive data center operators (MDCOs) serve DSSs. In this paper, we analyze the resource management problem in the multi-FN multi-MDCO, and multi-DSS networks. We model the network architecture with 3-layer model, where the FNs are in the upper layer, MDCOs in the middle layer, the DSSs in the bottom layer. The FNs first share computing resource with the MDCOs, and thus the MDCOs are able to serve their DSSs with low delay. Based on the model, we propose a hierarchical game, where the interaction between FNSs and MDCOs is regarded as a multi-leader multi-follower Stackelberg game, and the interactions between MDCOs and DSSs are regarded as the single-leader single-follower Stackelberg games. By making decisions distributively, all FNs, MDCOs, and DSSs receive high utilities. Simulation results show the correctness of the analysis and the performance improvement of the proposed strategies in the fog computing networks.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".