Acquisition of Virtual Machines for Tiered Applications with Availability Constraints
Bibliographic record
Abstract
Deploying and managing a high availability tiered application in the cloud is a challenging task because it requires determining and buying enough number of VMs dynamically such that the application is available. An application is available if it is working and it can respond in a timely manner for varying workloads. For a given workload, we will need a minimum number of working copies for each server and the minimum computing power of VMs necessary to run those copies for meeting the response time requirement. Otherwise, we will end up with response time failures. In this work, we assume that each software server of an application is replicated into one or more copies and each copy runs on a separate virtual machine (VM). VMs can be of different types depending on their computing power, availability and cost. This paper presents a novel optimization model to determine the number and types of VMs needed for each server that minimizes the cost and at the same time guarantees the availability SLA (service-level agreement). The results demonstrate that it is more cost effective to have a mixture of different types of VMs for running the copies of a server rather than restricting the copies to run on a single type of VMs. The results further demonstrate that the decision to buy only the cheapest VMs for an application is not always better cost-wise.
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 | 0.000 |
| 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".