Survivable IaaS Management with SDN
Bibliographic record
Abstract
Fault-tolerance, survivability and resiliency in wide area networks have long been prominent research topics. With the popularity of the cloud service model and the novel software defined networking (SDN) paradigm, there is renewed interest in failure protection and restoration in network service provisioning. In this work, we propose a novel protection and restoration based virtual network management scheme to enhance fault tolerance in infrastructure-as-a-service, deployed over networked cloud infrastructure. The networked cloud infrastructure is composed of multiple geographically distributed datacenters that are interconnected with SDN. Both compute and network resources are allocated by formulating the virtual network embedding problem as an integer linear program. A shared backup virtual link protection mechanism and a reactive traffic engineering network failure restoration algorithm are proposed and integrated with the framework to provide recovery from unexpected link failures. We implemented the framework on an emulated SDN testbed and evaluated the performances of the algorithms during single and multiple link failures. Experimental results demonstrate trade-offs of the proposed approaches and their applicability in different application scenarios.
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.
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 | 0.001 |
| 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 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".