OpenFlow supporting inter-domain virtual machine migration
Bibliographic record
Abstract
Today, Data Center Networks (DCNs) are re-architected in different new architectures in order to alleviate several emergent issues related to server virtualization and new traffic patterns, such as the limitation of bi-section bandwidth and workload migration. However, these new architectures will remain either proprietary or hidden in administrative domains, and interworking protocols will remain in-process of standardization for a time longer than the usually required time to market. Therefore, interworking cloud DCNs to provide the federated clouds is a very challenging issue that seems to be potentially alleviated by a software-defined networking (SDN) approach such as Openflow. In this paper, we propose a network infrastructure as a services (IaaS) software middleware solution based on Openflow in order to abstract the DCN architecture specifities and instantly interconnect DCNs. As a proof of concept we implement an experimental scenario dealing with virtual machine migration. Then, we evaluate the network setup and the migration delay. The use of the IaaS middleware allows automating these operations. OpenFlow solves the problem of interconnecting heterogeneous Data Centers and its implementation offers interesting delay values.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".