Undergraduate Engineering Computing Virtualization
Bibliographic record
Abstract
A new virtualised computer lab has been implemented for Engineering 1 students at McMaster University in Hamilton, Ontario, Canada.The laboratory infrastructure was deployed in two stages with the goal of providing a user experience that was indistinguishable from a traditional computing lab.The first stage was a one-to-one mapping between 56 thin clients and 56 Blade servers using the Teradici communication protocol.The second stage was a true virtualised platform with 56 thin clients and 3 Blade servers using the PCover-IP (PCoIP) communication protocol.We surveyed several laboratory sections on usability and user experience of a popular visual programming development environment and a solid modelling CAD package.Students were asked to compare their current "workstation" performance against previous performance and the performance of other computing facilities on campus.When conducting the survey, students were not aware whether the machine they were seated at was a stage-1 or stage-2 configuration.The results show the majority of users believe the stage-2 virtualised implementation is as good or better than stage-1 or other facilities on campus.We have also compared resource requirements for the traditional computer lab and the virtualised computer lab.Operational resources are reduced by several orders of magnitude and administration has become significantly more efficient.In the current third stage of deployment we will be allowing students to connect to the virtualised laboratory from outside the physical lab.Our paper will cover the motivation and benefits for developing this laboratory, the structure of our virtualised laboratory, the student survey results, and the calculations of operational savings using this new laboratory model.The implementation of a virtualised laboratory structure provides new flexibility on the accessibility to computing laboratories, which we believe will be of interest to all levels of engineering education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".