Stratus: Building and Evaluating a Private Cloud for a Real-World Financial Application
Bibliographic record
Abstract
Cloud computing technology has been emerging and spreading at a great pace due to its service oriented architecture, elasticity, cost-effectiveness, etc. Many organizations are using Infrastructure-as-a-Service (IaaS) public Clouds for data migration away from traditional IT Infrastructure but there are a few fields such as finance, hospitals, military and others that are reluctant to use public Clouds due to perceived security vulnerability. Enterprises in such fields feel more vulnerable to security breaches and feel secure using in-house IT infrastructure. The introduction of private Clouds is a solution for these businesses. Private Clouds have been substituted for the traditional IT Infrastructure due to its flexible ``pay-as-you-go" model within an organization by departments and enhanced privacy relative to public Clouds in the form of administration control and supervision. Goal of my thesis is to build and evaluate a private Cloud that can provide virtual machines (VMs) as a service and applications as a service. To achieve this goal, in my thesis, I have built and evaluated a service oriented IaaS model of private Cloud. I have used off-the-shelf servers and open-source software for this purpose. I have proposed a new replication strategy using an Openstack component called Cinder. My experiments show that efficient VM failure recovery on the basis of ``preparation delay" time can be achieved using my strategy. I have studied a real-world application of option pricing from the finance market and have used that application for the purpose of testing my private Cloud for compute workload and accuracy of the pricing results. Later, I have compared performance between Cloud VMs and standalone servers. The performance of Cloud VM is found to be better to standalone servers as long as the number of virtual CPUs (vCPUs) are limited to single node. Stratus clouds are groups of small clouds that collectively give a spectacular sight in the sky. The private Cloud I have built uses multiple small modules to achieve the stated goal and hence I named my private Cloud ``Stratus". This Stratus private Cloud is now ready for deploying applications, and for providing VMs on-demand.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".