How to buy a network: trading of resources in the physical layer
Bibliographic record
Abstract
Recently, a number of new research initiatives, most notably UCLPv2 and GENI, have promoted the dynamic partition of physical network resources (infrastructure) as the means to operate the network, and to implement new protocols and services. This has led to a number of open issues such as resource discovery, implementation of resource partitioning, and the aggregation of resources to create arbitrary network topologies. To us, the key issue is the design of a mechanism to trade, acquire, and control network resources, given a choice of providers of physical resources (infrastructure providers). In this article we present an architecture that allows physical resources to be traded, while granting users controlled access to the acquired resources via a policy enforcement mechanism. In addition, it allows resource provider domains to be linked via configurable, provider-neutral resource exchange points that are the physical resource equivalents of the pooling point, or Internet exchange point (IXP). We demonstrate how our trading system will operate by presenting a use case in which a network topology is constructed using resources from multiple providers, be it Internet service providers (ISPs), or National Research Experimental Network (NREN) providers. The use case also shows how a dynamic reconfiguration can be effected by the customer though the use of simple access control policies, without involving the provider
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".