CRAM: a Container Resource Allocation Mechanism for Big Data Streaming Applications
Bibliographic record
Abstract
Containerization provides a lightweight alternative to the use of virtual machines for potentially reducing service cost and improving cloud resource utilization. A key challenge is how to allocate container resources to multiple competing streaming applications with varying QoS demands running on a heterogeneous cluster of hosts. In this paper, we focus on workload distribution for optimal resource allocation to meet the real-time demands of competing containerized big data streaming applications. We propose a container resource allocation mechanism (CRAM) based on game theory and formulate the problem as an n-player non-cooperative game among a set of heterogeneous containerized streaming applications. From our analysis, we obtain the optimal Nash Equilibrium state where no player can further improve its performance without impairing others. Experimental results demonstrate the effectiveness of our approach, which attempts to equally satisfy each containerized streaming application's request as compared to existing techniques that may treat some applications unfairly.
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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 | 0.002 | 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 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".