ALTOCUMULUS: Scalable Scheduling for Nanosecond-Scale Remote Procedure Calls
Bibliographic record
Abstract
Online services in modern datacenters use Remote Procedure Calls (RPCs) to communicate between different software layers. Despite RPCs using just a few small functions, inefficient RPC handling can cause delays to propagate across the system and degrade end-to-end performance. Prior work has reduced RPC processing time to less than 1 $\mu$ s, which now shifts the bottleneck to the scheduling of RPCs. Existing RPC schedulers suffer from either high overheads, inability to effectively utilize high core-count CPUs or do not adaptively fit different traffic patterns. To address these shortcomings, we present ALTOCUMULUS,1a scalable, software-hardware codesign to schedule RPCs at nanosecond scales. ALTOCUMULUS provides a proactive scheduling scheme and low-overhead messaging mechanism on top of a decentralized user runtime. ALTOCUMULUS also offers direct access from the user space to a set of simple hardware primitives to quickly migrate long-latency RPCs. We evaluate ALTOCUMULUS with synthetic workloads and an end-to-end in-memory key-value store application under real-world traffic patterns. ALTOCUMULUS improves throughput by 1.3-24.6$\times$ under a 99thpercentile latencythpercentile latency $\lt 8.5\mu \mathrm{s}$.1Automatic Concurrent Migration Load-balancing Strategy (AutoCuMuLuS), homophonic with “altocumulus” as a type of clouds in meteorology, fragmented to separate patches or nodes.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".