Bibliographic record
Abstract
Datacenters of today have maintained the same architecture for decades. The building block of the datacenter remains the server, which tightly couples the necessary compute resources, memory, and storage to run its tasks. However, this traditional approach suffers from under-utilization of its resources, often caused by the over-provisioning of these resources when deploying applications. Datacenter operators allocate the worst-case amount of memory required for each deployed application, which lasts for the entirety of the application’s lifetime, even when not actually used. This causes servers to quickly, and falsely, run out of memory before their CPUs have been fully utilized. To address these problems, a new shift in the way datacenters are being built has been gaining more traction. Namely, memory disaggregation. Memory disaggregation can address these problems by decoupling the computational elements from the memory resources, allowing each to be provisioned and utilized separately. While the idea of memory disaggregation is not new, an increasing number of different proposals of memory disaggregation have seen the light in recent years. In this paper, we review many of these recent proposals, and study their architectures, implementations, and requirements. We also categorize them based on their features, and attempt to identify their strengths and shortcomings in an effort to highlight possible directions for future work and provide a reference for the research community.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".