Minimizing Effort and Risk with Network Change Deployment Planning
Bibliographic record
Abstract
Networks undergo continuous changes to introduce new services and improve existing ones. Network change deployment involves carefully deciding when each change activity will be executed and who will be executing the change. This is a complex process because each service group has to plan its activities following a set of operational and technological constraints. Besides, multiple groups may be working on the same or dependent nodes at the same time, and they must coordinate their deployment plans. If they do not co-ordinate, conflicting change execution could result in unexpected impacts. Traditionally, change deployment has been a tedious and time-consuming task. To address this, we propose an innovative solution Zapper that aims for minimal human effort to coordinate the changes, minimal risk to service quality, and efficient plans to rapidly deploy the changes. Zapper maps change scheduling constraints into mathematical equations and then uses optimization algorithms to generate conflict-free change plans that satisfy all constraints across service groups. We have deployed Zapper at a large service provider and it is being used regularly by the network operations teams for more than two years to schedule over 4.5 million change activities.
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".