Oasis: Performance Matching IoT System Emulation
Bibliographic record
Abstract
Internet of Things (IoT) and its applications are proliferating. Performance and scalability are key aspects of an IoT system. A scalable IoT system can gracefully accommodate a growth in the number of IoT devices while still meeting performance requirements. This motivates the need for tools that allow the performance and scalability of an IoT system to be evaluated prior to deployment. Implementing the actual system at full scale and then evaluating it through measurements is ideal from the point of view of realism. However, such an approach can be expensive and inflexible. Emulating an IoT deployment on commodity hardware is an attractive alternative since it allows various system design alternatives to be evaluated in a flexible way without the need for expensive full scale implementations of the alternatives. Recently, many such IoT emulation platforms have been proposed. However, none of these platforms are appropriate for performance and scalability testing since they do not provide a mechanism to match the performance characteristics of the IoT devices being emulated. We present Oasis, a system that addresses this limitation. Oasis uses Docker containers to emulate IoT devices. It leverages Docker's resource allocation and network emulation capabilities to match the performance characteristics of an emulated device to that of its native counterpart. We also show that the ability to accurately match performance improves scalability as well.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".