Topology and Application Aware Dynamic VM Management in the Cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cloud computing continues to mature and more applications continue to be deployed in public clouds. Client applications deployed in the cloud should automatically scale up and down to match changing workload demands, though they must be careful to ensure that sufficient resources are provisioned to achieve performance objectives. The cloud provider, on the other hand, attempts to reduce costs by reducing power consumption by consolidating load onto fewer, highly utilized machines. In this work, we introduce an algorithm that integrates both application autoscaling and dynamic virtual machine (VM) allocation into a single algorithm in order to achieve the goals of both cloud provider and client. Further, we consider multi-VM applications, such as multi-tiered web-based applications, and extend the integrated algorithm to take the network topology into account when placing or migrating applications. The goal is to reduce VM-to-VM communication latency; our focus is on trying to contain applications within the same racks. We evaluate our work through simulation, showing that the integrated algorithm can achieve better application performance with a significant reduction in virtual machine live migrations, and the topology-aware extension successfully places applications within a single rack.
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.
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 | 0.001 | 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 | 0.002 | 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 it