Autonomicity in Virtual Private Network provisioning for enterprises
Bibliographic record
Abstract
Large enterprises usually require Virtual Private Network (VPN) services provisioned by the network operator. Also, there is an emerging need for supporting multicast communications, i.e. one host communicate with other hosts located in multiple remote sites. While MPLS-based IP VPNs are proven to be scalable, current approaches for extending it with multicast features involve potential state explosion, some bandwidth inefficiencies in the operator network or complex management tasks to find a good balance between forwarding state and bandwidth usage. These properties are direct consequences of the current MPLS and network-layer multicast forwarding approaches, as state should be maintained in the forwarding plane for each tree in each intermediate node. In this paper, we build on a stateless Bloom-filter-based forwarding plane installed in the service provider's network. By moving the state into the packet headers from the nodes, new trade-offs appear due to the probabilistic nature of Bloom filters. We highlight autonomic scenarios, such as self-configuration of addresses, resource management in the network, simple autonomic provisioning of dynamic multicast trees and self-optimization of forwarding performance.
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 | 0.001 | 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".