Performance of protected working capacity envelopes based on p -Cycles: fast, simple, and scalable dynamic service provisioning of survivable services
Bibliographic record
Abstract
As an alternative of the Shared Backup Path Protection (SBPP) method, we develop a framework for dynamic provisioning of survivable services based on the use of p-cycles to form a Protected Working Capacity Envelope (PWCE) within which dynamic provisioning of protected services is greatly simplified. Based on p-cycles, the restoration speed of rings is obtained, but with the capacity efficiency of shared-mesh networks. In addition, with PWCE, arbitrarily fast dynamic service demands can be handled with much less complexity (in terms of database dependency and state update dissemination) than under SBPP. Only a simple OSPF-topology view of non-exhausted spans in the envelope is required. If a new path can be routed through the envelope, it is protected by virtue of being routable. This is in contrast to needing a full database of network state so that the end-user can set up a shared backup protection path under SBPP. In addition, dissemination of state updates occurs only on the time-scale of the non-stationary evolution of the demand statistics, not on the time-scale of individual connections. During statistically stationary periods, there is no dissemination of state updates whatsoever with an envelope that is well matched to its load. The PWCE concept thus offers some new tradeoffs between operational simplicity and spare capacity efficiency. The main contribution of this work is the detailed implementation and simulation of test networks operating under PWCE and designed with novel envelope volume maximizing formulations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".