Enabling A Load Adaptive Distributed Stream Processing Platform on Synchronized Clusters
Bibliographic record
Abstract
Distributed stream processing (DSP) platforms enable simplified development of applications that can process continuous unbounded streams of data at a high speed. Leveraging large scale cluster management frameworks, DSP can scale to analyze data in real-time with different types of operators, each running on a cluster node. The scalability and resource utilization depend on the allocation of operators on clusters. Since the data volume and rate can be unpredictable, static mapping between operators and cluster resources results in unbalanced operator load distribution. This paper proposes a software layer that is load-adaptive between a DSP platform and clusters. It allows dynamic transferring of an operator to different cluster nodes at runtime and keeps the process transparent to developers. We present a prototype implemented on Yahoo's S4. Our implementation is evaluated by a top-N topic list application on Twitter streams. The results demonstrate improved stream processing throughputs and cluster resource utilization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".