Towards a decentralized algorithm for mapping network and computational resources for distributed data-flow computations
Bibliographic record
Abstract
Several high-throughput distributed data-processing applications require multi-hop processing of streams of data. These applications include continual processing on data streams originating from a network of sensors, composing a multimedia stream through embedding several component streams originating from different locations, etc. These data-flow computing applications require multiple processing nodes interconnected according to the data-flow topology of the application, for on-stream processing of the data. Since the applications usually sustain for a long period, it is important to optimally map the component computations and communications on the nodes and links in the network, fulfilling the capacity constraints and optimizing some quality metric such as end-to-end latency. The mapping problem is unfortunately NP-complete and heuristics have been previously proposed to compute the approximate solution in a centralized way. However, because of the dynamicity of the network, it is practically impossible to aggregate the correct state of the whole network in a single node. In this paper, we present a distributed algorithm for optimal mapping of the components of the data flow applications. We propose several heuristics to minimize the message complexity of the algorithm while maintaining the quality of the solution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".