Resource Optimization of Stream Processing in Layered Internet of Things
Bibliographic record
Abstract
IoT (Internet of Things) applications often involve stream processing using multiple complex layers of processing nodes, where in each layer, data is received by the nodes, processed, and then transmitted to the nodes in subsequent layers. Such systems present a tradeoff between reliability and resource usage, including CPU power, energy, network band-width, memory, etc. Reducing the reliability at which a node processes inbound data in a layer can have repercussions on nodes in subsequent layers in the network that receive less reliable data, and in turn impact the reliability of the application as a whole. In this paper, we present a generalized model of streaming IoT applications as a layered network of producers and consumers. Our model captures trade-offs between reliability and resource usage of the system. We present an efficient algorithm using SMT constraint solvers to determine the optimal selection of processing quality for each node in the network, such that target system reliability is achieved while respecting the given resource bounds, and resource usage is minimized. In addition, we present a lightweight machine learning based solution to drastically improve our model in terms of run time. We have fully implemented our technique and report experimental results on a layered IoT network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".