RACE: Relinquishment-Aware Cloud Economics Model
Bibliographic record
Abstract
A lot of work on resource estimation has been carried out in the area of cloud computing. For example, some recent models assign resources based on the history of the users and the utilization of the cloud resource pool. Although these models have generally shown an increase in server utilization, they lack a cost-benefit analysis to know the profit margin obtained by the respective resource allocation schemes. Therefore, a complete model to analyze the cost could be a valuable tool for IaaS cloud service providers (CSP) to compare various resource assignment mechanisms. In this paper, we introduce a Relinquishment-Aware Cloud Economics Model (RACE) to calculate the net profit in a cloud provider environment. Our model includes various parameters such as service price, income from resources used by cloud service customers (CSC), service utilization, number of servers, electricity cost, and service relinquishment cost. The noteworthy contribution of our model is that it includes the cost incurred when users are leaving the cloud provider before their scheduled end time. We consider this loss as relinquishment cost or opportunity cost loss. After implementing our model, we evaluate different resource allocation schemes in a finite resource pool environment. The preliminary results show that blindly assigning more resources does not necessarily generate more profit.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".