Offering Resilient and Bandwidth Guaranteed Services in Multi-tenant Cloud Networks: Harnessing the Sharing Opportunities.
Bibliographic record
Abstract
The sharing of computing and networking resources in the cloud is challenged by several obstacles, such as providing bandwidth guarantees for a predictable performance of the hosted applications, as well as maintaining the availability of their services following outages. Therefore, the wide scale adoption of this emerging computing paradigm remains highly dependent on overcoming these challenges. In fact, a lack of bandwidth guarantees extends the completion time for jobs, thus increasing expenses for clients paying for their time of use. In addition, outages in data centers may result in severe revenue losses for both, the cloud operators and their clients alike. To overcome these challenges, cloud operators should be empowered with a strategic design plan that is able to guarantee resilient and predictable performance for hosted applications. Such a plan consists of provisioning additional backup resources (e.g.virtual machines, bandwidth) while ensuring efficient network bandwidth utilization. In this work, we study the design of various facets of such a plan. Namely, we exploit several bandwidth sharing opportunities in multi-tenant cloud networks while offering resilient and bandwidth guaranteed services. In contrast to previous works which target cloud clients satisfaction, we focus on optimizing network bandwidth utilization in order to increase the cloud operators revenues while maintaining such bandwidth allocation transparent to the clients. Through several motivational examples, and numerical studies, we highlight the sharing opportunities and show that they are able to increase cloud operators revenues by an average of 21.4% while providing up to 50% of bandwidth gain in the network.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".