OCReM: OpenStack-based cloud datacentre resource monitoring and management scheme
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
Managing virtualised computing, network and storage resources at large-scale in both public and private cloud datacentres is a challenging task. As an open source cloud operating system, OpenStack needs to be enhanced for managing cloud datacentre resources. In order to improve OpenStack functions to support cloud datacentre resource management, we present OCReM: OpenStack-based cloud datacentre resource monitoring and management scheme. First, we designed a virtual machine group life-cycle management module. Then, we designed and developed a cloud resource monitoring module based on the Nagios monitoring software and Libvirt interface. We conducted an integrated experiment to verify the performance improvement of group-oriented auto scaling and elastic load balancing policy based on real-time resource monitoring data. After that, we implemented the OCReM-EC2 hybrid cloud monitoring and auto scaling model. Finally, we analysed the prospective research direction and propose our future work.
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 | 0.000 | 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