Critical Path Analysis through Hierarchical Distributed Virtualized Environments Using Host Kernel Tracing
Bibliographic record
Abstract
The dynamic nature of applications in Virtual Machines (VMs) and the increasing demand for virtualized systems make the analysis of dynamic environments critical to achieve efficient operation of such complex distributed systems. In this article, we propose a precise host-based tracing and analysis method to retrieve execution flows, and dependency flows from virtualized environments, regardless of the level of nested virtualization. Given a host operating system level trace, the Any-Level vCPU Detection (ASD) algorithm and Guest Thread-state Analysis (GTA) algorithm detect the different states of vCPUs and threads for arbitrary nesting depths. Then, the Execution-graph Construction (HEC) algorithm extracts the waiting / wake-up dependencies chains out of the running processes across VMs, for any level of virtualization in a transparent manner. The process dependency graph, vCPU state, and VM process state are displayed in an interactive trace viewer, Trace Compass, for further inspection. Our proposed VM trace analysis algorithms have been open-sourced for further enhancements and collaborative research and development. Our new techniques were evaluated with workloads generated using several well-known server applications (e.g., Hadoop, Apache, MySQL, Linux apt-get, and IMS network). The proposed approaches are based on host hypervisor tracing, which brings a lower tracing overhead (around 1 percent), is easier to deploy, and presents fewer security issues as compared to other approaches.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".