Micro-Sliced Virtual Processors to Hide the Effect of Discontinuous CPU Availability for Consolidated Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although time-sharing CPUs has been an essential technique to virtualize CPUs for threads and virtual machines, most of the commercial operating systems and hyper visors maintain relatively coarse-grained time slices to mitigate the costs of context switching. However, the proliferation of system virtualization poses a new challenge for the coarse-grained time sharing techniques, since operating systems are running on virtual CPUs. The current system stack was designed under the assumption that operating systems can seize CPU resources at any moment. However, for the guest operating system on a virtual machine (VM), such assumption cannot be guaranteed, since virtual CPUs of VMs share limited physical cores. Due to the time-sharing of physical cores, the execution of a virtual CPU is not contiguous, with a gap between the virtual and real time spaces. Such a virtual time discontinuity problem leads to significant inefficiency for lock and interrupt handling, which rely on the immediate availability of CPUs whenever the operating system requires computation. This paper investigates the impact of virtual time discontinuity problem for lock and interrupt handling in guest operating systems. To reduce the gap between virtual and physical time spaces, the paper proposes to shorten time slices for CPU virtualization to reduce scheduling latencies of virtual CPUs. However, shortening time slices may lead to the increased overhead of context switching costs across virtual machines. We explore the design space of architectural solutions to reduce context switching overheads with low-cost context-aware cache insertion policies combined with a state-of-the-art context prefetcher.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| 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 it