Tech report: Orchestrating your cloud orchestra: Model driven development of cloud deployment and orchestration for distributed computer music instruments
Bibliographic record
Abstract
Cloud computing potentially ushers in a new era of computer music performance with exceptionally large computer music instruments consisting of 10s to 100s of virtual machines called a Cloud Orchestra. Cloud computing allows for the rapid provisioning of resources, but to deploy such a complicated and interconnected network of software synthesizers in the cloud requires a lot of manual work, system administration knowledge, and devops (developer-sysop) skills. This is a barrier to computer musicians whose goal is to produce and perform music, and not to sysadmin 100s of computers. This work discusses the issues facing cloud orchestra deployment and offers an abstract solution and a concrete implementation. The abstract solution is generate cloud orchestra deployment plans by allowing computer musicians to model their network of synthesizers and to describe their resources. A model optimizer will compute near-optimal deployment plans to synchronize, deploy, and orchestrate the start-up of a complex network of synthesizers deployed to many computers. This model driven development approach frees computer musicians from much of the hassle of deployment and allocation. Computer musicians can focus on the configuration of musical components and leave the resource allocation up to the modelling software to optimize.
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 | 0.002 | 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 | 0.000 | 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".