Fundy: A Scalable and Extensible Resource Manager for Cloud Resources
Bibliographic record
Abstract
Scalability is an important property for a resource manager in the cloud. It is challenging to manage large-scale cluster resources while serving a large number of requests. Moreover, cloud-based applications and new technologies result in changeable requirements, so a cloud resource manager needs to continuously evolve. This paper presents a novel architecture design for a scalable and extensible resource manager. Our resource manager named Fundy employs a microservices architecture based on an in-memory data grid. The in-memory data grid accelerates data processing and enables Fundy to easily scale out. The data grid facilitates Fundy's microservices-based architecture. Because of the architecture, it is possible to rapidly deploy new features in Fundy. Fundy also enables multiple schedulers to schedule different workloads on shared infrastructure. In addition, we introduce a new packing algorithm to improve the allocation quality of our scheduler and a tensor scheduling algorithm to speed up parallel processing of requests by orders of magnitude. Fundy has been deployed in our production public cloud. This paper includes evaluation based on real-world traces and the results highlight the advantages of Fundy.
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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 | 0.000 | 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".