Cost-Effective Cloud Resource Provisioning Using Linear Regression
Bibliographic record
Abstract
In the era of cloud computing, accessing virtual computing resources has become increasingly convenient for users to meet their demands. Cloud providers offer two primary payment plans for virtual resource provisioning: reservation and on-demand. The reservation plan requires users to reserve resources and pay upfront, making it more cost-effective for long-term requirements despite demand uncertainty. Conversely, the on-demand plan charges based on actual resource usage, making it more expensive but suitable for short-term needs. Efficient resource provisioning is crucial to balance user demands and costs, as inefficient provisioning can lead to high costs. A key challenge is determining the optimal number of resources to reserve to accommodate uncertain demands while minimizing costs. This paper addresses the resource reservation problem in cloud environments by focusing on the optimal reservation of virtual machines (VMs). We propose a linear regression approach that fits a linear function to features such as past demands and previously reserved instances that are still available to determine the quantity of VMs to reserve. Our model assigns specific weights to these features to predict required reserved instances, minimizing the overall cost, including both the expense of reserving resources and renting additional on-demand resources as needed. Our evaluation, based on real standard workload traces, demonstrates the effectiveness of our approach in achieving cost-efficient resource provisioning, reducing the total cost by efficiently balancing reserved and on-demand resources.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".