Proactive inter-datacenter multicast with realtime and bulk transfers
Bibliographic record
Abstract
In content distribution networks, a key objective is the efficient utilization of the network that interconnects geographically distributed datacenters. This is a challenging problem due to vastly different characteristics and requirements of bulk and realtime transfers that share the interconnection network. Bulk transfers aim at delivering a copy of a usually large file to multiple datacenters before a deadline, while realtime transfers are absolutely delay-intolerant with unsteady and dynamic demands. In this paper, we consider the problem of multicasting deadline-critical bulk transfers in an inter-datacenter network in the presence of unknown and fluctuating demand by realtime transfers. Specifically, we develop a joint admission control and routing algorithm called PMDx, which anticipates future realtime demands and proactively reserves just the right amount of network resources in order to serve future realtime transfers without adversely affecting network utilization or bulk transfer deadlines. We show that the PMDx algorithm is a 2/δ-approximation with probability 1 - ϵ, and runs in polynomial time proportional to ln(1/ϵ)/(1 - δ)2, for 0 < δ,ϵ < 1. We also provide extensive model-driven simulation results to study the behaviour of our algorithms in real world network topologies. Our results confirm that PMDx is very close to the optimal, and improves the utilization of the network by 14% compared to a recently proposed algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".