SMART: Dual-channel Southbound Message Delivery in Clouds with Rate Estimation
Bibliographic record
Abstract
Driving southbound messages from a cloud control plane down to the distributed data plane on every compute node is one of the critical challenges in public clouds. Existing message delivery solutions solely based on remote procedure call (RPC) or message queue (MQ) tend to overlook strict resource constraints, e.g., network bandwidth and CPU capacity. This often results in extensive overhead in the control plane or message redundancy in the data plane, especially when a cloud receives highly concurrent user requests or experiences a rapid expansion. To this end, we design a dual-channel southbound message delivery framework, namely SMART, which combines an RPC channel with an MQ channel, to maximize the resource utilization in the cloud network. In the control plane, we implement a message parsing mechanism and propose a delivery channel selection algorithm based on the deep reinforcement learning (DRL) approach to support efficient dual-channel delivery under resource constraints. In the data plane, we design a message agent on each compute node to ensure the order preservation and state consistency of southbound messages. Both experimental and large-scale simulation results show that SMART demonstrates a reduction in control plane overhead by 64% compared to RPC and redundant messages by 45% compared to MQ, respectively.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".