IMPLEMENTASI OPENSTACK UNTUK INFRASTRUKTUR PRIVATE CLOUD COMPUTING
Bibliographic record
Abstract
Perkembangan Teknologi cloud membawa perubahan dalam berbagai model layanan. Saat ini cloud bukan hanya dapat digunakan sebagai infrastruktur (IaaS), Platform (Paas), dan software (SaaS). Namun mulai dapat digunakan hanya pada fungsi tertentu seperti cloud function as a service (FaaS). Hadirnya teknologi cloud ini semakin memudahkan institusi untuk mengembangkan layanan sendiri yang mereplikasi fungsi dari layanan cloud. Dengan ketersediaan engine opensource seperti openstack, opennebula, dan openshift, proses membangun infrastruktur mandiri untuk layanan cloud semakin terjangkau dari sisi biaya. Openstack merupakan open sources software yang dapat dikembangkan secara mandiri dengan berfokus pada infrastruktur (IaaS) karena menyediakan beragam fitur yang memadai. Penelitian ini menggunakan metode eksperimen dalam membangun layanan IaaS dengan openstack, pengujian yang dilakukan berupa pembuatan mesin virtual, monitoring mesin VM, serta uji performa mesin VM. Dari hasil penelitian, penggunaan mesin peneliti dapat memadai untuk menjalankan 10 mesin VM dengan stabil, dan rata-rata waktu untuk booting time 30 detik. Kata Kunci: Cloud Computing, Infrastructure as a Service, Openstack, Private cloud computing
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".