Joint Multicast Application-Group Mapping and Rendezvous Point Selection for Multi-Tenancy Data Center Networks
Bibliographic record
Abstract
Modern cloud applications frequently reside in overlay networks and exhibit one-to-many communication patterns. The Ingress Replication (IR) approach can support overlay multicast traffic by replicating packets at the ingress node and sending a unicast copy of the packet to each interested receiver. However, IR consumes more bandwidth as multiple copies of the same packet are forwarded through the network. The tenant-routed multicast architecture addresses this drawback by leveraging IP multicast in the underlay network to transport overlay multicast application traffic. The overlay multicast traffic is encapsulated with an underlay multicast group address and then transported across the network without duplication. However, the limited number of underlay multicast groups makes it challenging to efficiently support a massive number of multicast applications, which is typical in cloud environments. In this paper, we address the problem of mapping overlay applications to underlay multicast groups and selecting Rendezvous Points (RPs) to minimize network utilization. We provide a mathematical formulation for this joint problem and design a local search-based algorithm to solve it. Extensive experimental results demonstrate that our solution can approximate the exact solution and significantly reduce network utilization compared to conventional thresholdbased methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".