Bibliographic record
Abstract
When an application deployed in the cloud faces changing workload, the services of the application need scaling up or down in response. The services run on Virtual Machines (VM) or container instances. Application Providers (APs) decide on how the applications are scaled through VM provisioning and through the placement of the services on those VMs. Various drivers guide this decision making. Application performance and cost are two such drivers. In this paper, we answer the question of how APs can meet the performance constraints of their applications while minimizing the cost of the running VMs. A VM provisioning problem is formulated which expects to meet mean response time constraints and minimize the cost, where VM-types having different cost rates are used. The proposed solution is based on genetic algorithm and bottleneck strength value. For the case study, a decision maker is implemented for a web application. The proposed solution is compared against an exhaustive search, a simple genetic algorithm and a random search. It is shown that our solution is able meet response time constraints with near optimal minimization of cost. The solution also results in better cost than random search and the plain genetic algorithm solution at the expense of slightly longer runtime.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".