Virtual machine scheduling and migration management across multi-cloud data centers: blockchain-based versus centralized frameworks
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
Abstract Efficiently managing virtual resources in the cloud is crucial for successful recourse utilization. Scheduling is a vital technique used to manage Virtual Machines (VMs), enabling placement and migration between hosts located in the same or different data centers. Effective scheduling not only ensures better server consolidation but also enhances hardware utilization and reduces power consumption in data centers. However, scheduling VMs across a Wide Area Network (WAN) poses considerable challenges due to connectivity issues, slower communication speeds, and concerns around data integrity and confidentiality. To enable informed scheduling decisions, it is critical to facilitate the exchange of real-time and accurate status information between cloud data centers, ensuring optimal resource allocation and minimizing latency. To address this, we propose a novel distributed cloud management solution that utilizes blockchain technology to facilitate efficient sharing of VM characteristics across multiple data centers. BigchainDB platform has been used as a blockchain-based ledger database to effectively share information required for VM scheduling and migration across different data centers. The proposed framework has been validated and compared with a Virtual Private Network (VPN)-based centralized management solution. The proposed model utilizing blockchain-based solution achieves 41.79% to 49.85% reduction in number of communication messages and 2% to 12% decrease in total communication delay comparing to the centralized model.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 it