Bibliographic record
Abstract
The number of publicly accessible virtual execution environments (VEEs) has been growing steadily in the past few years. To be accessible by clients, such VEEs need either a public IPv4 or a public IPv6 address. However, the pool of available public IPv4 addresses is nearly depleted and the low rate of adoption of IPv6 precludes its use. Therefore, what is needed is a way to share precious IPv4 public addresses among a large pool of VEEs. Our insight is that if an IP address is assigned at the time of a client DNS request for the VEE's name, it is possible to share a single public IP address amongst a set of VEEs whose workloads are not network intensive, such as those hosting personal servers or performing data analytics. We investigate several approaches to multiplexing a pool of global IP addresses among a large number of VEEs, and design a system that overcomes the limitations of current approaches. We perform a qualitative and quantitative comparison of these solutions. We find that upon receiving a DNS request from a client, our solution has a latency as low as 1 ms to allocate a public IP address to a VEE, while keeping the size of the required IP address pool close to the minimum possible.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".