Dynamic Task Scheduling and Adaptive GPU Resource Allocation in the Cloud
Bibliographic record
Abstract
The growing demand for computational power in cloud computing has made Graphics Processing Units (GPUs) essential for providing substantial computational capacity. Efficiently allocating GPU resources is crucial due to their high cost. Additionally, it’s necessary to considercloud environment characteristics, such as dynamic workloads, multi-tenancy, and requirements like isolation. One key challenge is efficiently allocating GPU resources while maintaining isolation and adapting to dynamic workload fluctuations. Another challenge is ensuring scheduling maintains fairness between tenants while meeting task requirements (e.g., completion deadlines). While existing approaches have addressed each challenge individually, none have tackled both challenges simultaneously. This is especially important in dynamic environments where applications continuously request and release GPU resources. This paper introduces a new dynamic GPU resource allocation method, incorporating fair and requirement-aware task scheduling. We present a novel algorithm that leverages the multitasking capabilities of GPUs supported by both hardware and software. The algorithm schedules tasks and continuously reassesses resource allocation as new tasks arrive to ensure fairness. Simultaneously, it adjusts allocations to maintain isolation and satisfy task requirements. Experimental results indicate that our proposed algorithm offers several advantages over existing state-of-the-art solutions. It reduces GPU resource usage by 88% and significantly decreases task completion times.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".