Dynamic inter-SLA resource sharing in path-oriented differentiated services networks
Bibliographic record
Abstract
This paper proposes novel resource sharing schemes for differentiated services (DiffServ) networks, to achieve both high resource utilization and quality of service (QoS) guarantee. Service level agreements (SLAs) are negotiated at network boundaries and supported by path-oriented resource mapping within the network. The recently proposed SLA management scheme based on virtual partitioning (Bouillet et al., 2002) allows overloaded SLAs to exploit the spare capacity of underloaded SLAs for efficient resource utilization, however, at the the cost of possible SLA violation of the underloaders. In the bandwidth borrowing scheme proposed here, the dedicated bandwidth for underloaded SLAs is determined and adaptively adjusted at network boundaries according to the actual traffic load and QoS policies; the available spare capacity is then properly distributed to related links for lending to others. On the other hand, the traffic flows admitted with borrowed bandwidth are tagged and may be preempted later when the original bandwidth owner needs to claim back the resources. Through a detailed implementation design and extensive computer simulation results we show that, by bandwidth borrowing, both SLA compliance and high resource utilization can be achieved in various load conditions, with some side benefits such as call-level service differentiation, small admission overhead, and convenience for policy-based management. In addition, we propose a distributed bandwidth pushing scheme that can dynamically adjust the spare bandwidth distribution over the network. Combining bandwidth pushing with bandwidth borrowing, the resource utilization can be further improved.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".