Virtual CPU State Detection and Execution Flow Analysis by Host Tracing
Bibliographic record
Abstract
ABSTRACT: Cloud computing offers to the end user the ability of accessing a pool of resources with the Pay as Use (PaU) model. By leveraging this technology, users can benefit from hardware virtualization for on-demand resource acquisition and rapid elasticity. However, there is no effective tool to analyze virtual hardware performance, especially when isolation between these virtual resources is not adequate. The existing tools need to access and trace the whole activity of the VM and host. However, in most cases, tracing the virtual machine (VM) is not possible because of security issues and the added overhead. Therefore, there is a need for a tool to troubleshoot unexpected behavior of VMs without internal access for tracing or debugging. In this paper, we propose a new method to study the state of CPUs inside VMs without internal access. Our tool can detect unexpected delays and their root causes. We developed a virtual CPU (vCPU) state analyser to detect the state of vCPUs along with the reason for being in that state. This approach relies on host tracing, thus adding less overhead to VMs as compared to existing approaches. Then we propose a new approach for profiling threads inside the VMs by host tracing. We implemented different views for the TraceCompass trace viewer to let the administrator visually track different threads and their states inside the VMs. Our tool can detect different problems such as overcommitment of resources.
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 | 0.000 |
| 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 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".