Capacity allocation in service overlay networks
Bibliographic record
Abstract
Because of its decentralized nature and the lack of the required cooperations among the Internet autonomous systems, the current Internet is unable to provide end-to-end QoS guarantees to the application layer.The Service Overlay Network (SON) is a proposed solution to this end-to-end QoS provisioning problem.With its special overlay structure, the Service Overlay Network is able to provide reliable end-to-end QoS support on top of the Internet infrastructure.By considering the SON as a network with virtual connections and bandwidth allocations, we derive a series of new results for the SON.First, by referring to the reliability theory, we obtain a general form for the end-to-end blocking function.With the general blocking function, we investigate the two optimization approaches for designing a SON, namely the Maximum Profit (MP) approach that maximizes the profit and the Minimum Cost (MC) approach that minimizes the investment.Our study reveals that though the two approaches have been shown to be equivalent in many other settings, they are different in the SON environment.Our result indicates that the MP approach is a more appropriate approach for designing a SON, and the result is independent of the routing algorithm employed.Second, we develop a novel event dependent routing scheme that is efficient enough to be incorporated into the optimal capacity allocation problem.This allows the SON design problem to be formulated and solved as a MP optimization problem.Then we improve the optimization methodology by decomposing the main SON design problem into a number of sub-problems and we solve the main design problem by solving the sub-problems which involve finding the link shadow prices of the network links.It is exactly because we are using the maximum profit approach, the idea of link shadow price could be incorporated to solve the design problem.A fast macro-state convolution scheme based on the link shadow price is developed to provide a traffic differentiation module to the optimization formulation.This additional module allows the optimization framework consider traffic connections based on the (monetary) contributions they offer to the network.Like many other telecommunications services, SON is believed to exhibit positive network externalities -once the network reaches a "critical mass", it will continue to grow in a self-reinforcing manner.The optimal pricing of the SON services is therefore another crucial piece of information for the SON to achieve success.We study a set of Lagrange Contents ix D.1.2Double-Parameter Two-Side Bounds for the First Derivative of Erlang-B formula . . . . . . . . . . .
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".