Benefits and challenges of three cloud computing service models
Bibliographic record
Abstract
Cloud computing can be defined as the use of new or existing computing hardware and virtualization technologies to form a shared infrastructure that enables web-based value added services. The three predominant service models are infrastructure, platform, and software-asa-service. Infrastructure-as-a-Service (IaaS) can be defined as the use of servers, storage, and virtualization to enable utility like services for users. Security is a big concern within IaaS, especially considering that the rest of the cloud service models run on top of the infrastructure and related layers. Platform-as-a-Service (PaaS) providers offer access to APIs, programming languages and development middleware which allows subscribers to develop custom applications without installing or configuring the development environment. Software-as-a-Service (SaaS) gives subscribed or pay-peruse users access to software or services which reside in the cloud and not on the user's device. Understanding the cloud service models is critical in determining if cloud services or hosting are an appropriate business solution, and if so, which model best balances the level of control required versus reduced hardware, configuration, and maintenance costs. Cloud computing offers many benefits to organizations; it has enabled collaboration amongst disparate communities and workgroups, and has overcome challenges that have plagued existing business solutions. However, the security, privacy, and integrity of the cloud are of prime importance and there are many challenges that exist. At the present time there seems to be a lot of momentum behind the adoption of cloud computing despite these. This may simply be a trend, an indication that society truly wants their data to be available whenever from anywhere, or a sign that few understand the associated risks.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".