Characterizing network performance of single-node large-scale container deployments
Bibliographic record
Abstract
Cloud services have shifted from complex monolithic designs to hundreds of loosely coupled microservices over the last years. These microservices communicate via pre-defined APIs (e.g., RPC) and are usually implemented on top of containers. To make the microservices model profitable, cloud providers often co-locate them on a single (virtual) machine, thus achieving high server utilization. Despite being overlooked by previous work, the challenge of providing high-quality network connectivity to multiple containers running on the same host becomes crucial for the overall cloud service performance in this scenario. For that reason, this paper focuses on identifying the overheads and bottlenecks caused by the increasing number of concurrent containers running on a single node, particularly from a networking perspective. Through an extensive set of experiments, we show that the networking performance is mostly restricted by the CPU capacity (even for I/O intensive workloads), that containers can largely suffer from interference originated from packet processing, and that proper core scheduling policies can significantly improve connection throughput. Ultimately, our findings can help to pave the way towards more efficient large-scale microservice deployments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".