Virtual machine scheduling and migration management across multi-cloud data centers: blockchain-based versus centralized frameworks
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".