Optimal Routing for Competitive Service Providers in Network Virtualization Context
Bibliographic record
Abstract
In the context of Network Virtualization where service providers may instantiate virtual networks on a common physical infrastructure, multiple virtual servers may be instantiated on physical ones. These virtual servers are in different locations and provide the same service. Then, clients are served without knowing which server is replying and which path is crossed for their traffic routing. Service providers such as competitive ones who lease the physical infrastructure are responsible for adopting the suitable routing strategy. These providers aim to reduce the cost of leased resources while guaranteeing their clients' quality of service requirements. In this paper, we consider the optimal routing problem for competitive service providers in Network Virtualization context. We model it as a mixed integer linear program whose objective function is to minimize the cost under flow and servers' bandwidth constraints. We then solve the proposed program on a small instance of network topology. We notice that Traffic Concentration on the closest server yields an optimal solution: all the traffic of each client should be routed to the closest server in terms of hop count. We finally compare, through analytical models based on the M/D/1/N queue, the performance of Traffic Concentration with three load sharing techniques as a function of traffic intensity. We note that Traffic Concentration reduces the mean sojourn time for all the traffic intensities. However, the load sharing techniques outperform Traffic Concentration in terms of blocking probability for medium and heavy traffic intensities with comparable results for low traffic intensities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".