TARA: Tenant-Aware Resource Allocation in Multi-Tenant Data Centers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-Tenant Data Centers (MTDCs) allocate resources to tenants in terms of processors, memory, and storage. However, equal allocation of network resources is often overlooked, leading to unpredictable application performance. To address this issue, we propose Tenant-Aware Resource Allocation (TARA), a virtual resource allocation mechanism for MTDCs. TARA allocates tenants’ virtual network resources as virtual ports on the substrate physical network, enabling control and management by dedicated controllers. In this paper, we introduce a classification method for virtual nodes within Virtual Data Centers (VDCs) aimed at ensuring optimal network performance based on tenant demands. Furthermore, we present a source routing mechanism that utilizes path tables to minimize traffic forwarding delays and enhance network workload efficiency. The TARA model optimizes virtual resource allocation, enhances network performance, and simplifies virtual network resource management. Experimental evaluations demonstrate the effectiveness of the TARA system in improving network performance and meeting tenants’ quality of service requirements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it