An Efficient Survivable Design With Bandwidth Guarantees for Multi-Tenant Cloud Networks
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
In cloud data centers (DCs), where hosted applications share the underlying network resources, network bandwidth guarantees have shown to improve predictability of application performance and cost. However, recent empirical studies have also shown that often DC devices and links are not all that reliable and that failures may cause service outages, rendering significant revenue loss for the affected tenants, as well as the cloud operator. Accordingly, cloud operators are pressed to offer both reliable and predictable performance for the hosted applications. While much work has been done on solving both problems separately, this paper seeks to develop a joint framework by which cloud operators can offer both performance and availability guarantees for the hosted tenants. In particular, this paper considers a simple model to abstract the bandwidth guarantees requirement for the tenant and presents a protection plan design which consists of backup virtual machines (VMs) placement and bandwidth provisioning to optimize the internal DC traffic. We show through solid motivational examples that finding the optimal protection plan design is highly perplexing, and encompasses several constituent challenges. Owing to its complexity, we decompose it into two subproblems, and solve them separately. First, we invoke a placement subproblem of the minimum number of backup VMs and then we explore the most efficient correspondence between backup and primary VMs (i.e., protection plan) which minimizes the bandwidth redundancy. Further, we study the design of various facets of such a plan by exploiting bandwidth sharing opportunities in multi-tenant cloud networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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