Automated Deployment of CBC/Radio-Canada’s Media-Over-IP Data Center
Bibliographic record
Abstract
CBC/Radio-Canada is finalizing its new Internet Protocol (IP)-based production center in Montréal. During the design and early deployment of this major facility, it became clear that the staging and configuration of the thousands of media devices should be automated and managed in a fashion similar to an information technology (IT) data center. In fact, these new devices require thousands of parameters to be configured, and there are more frequent updates than for conventional devices. Moreover, once the system is in place, business continuity imposes careful management of system changes to minimize the risk of technical regressions and human errors. The good news is that the IT industry has solved the problem of operating huge data centers that require high availability. Continuous integration and continuous deployment (CI/CD) practices have proven track records for operating data centers throughout their lifecycle, from configuration and provisioning, updates and changes, to sanity checks and monitoring. Tools such as Dynamic Host Configuration Protocol (DHCP), domain name server (DNS), IP address management (IPAM), and configuration management tools are mature and widely used. This paper presents the architecture and implementation of CBC’s automated deployment workflow. We cover requirements on endpoint devices and the technical and human-factor challenges we encountered during our journey putting in place the novel approach for the media facility. We believe that these tools and methods will be applicable as a way forward to many media-over-IP projects at all scales.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".