Djenne: Dependable and Decentralized Computation for Networked Embedded Systems
Bibliographic record
Abstract
How should we build applications for large-scale networked embedded systems -- now in the incarnation of the Internet of Things -- when we do not want to rely on the existence of a persistent connection to a remote data center? We present the design and implementation of a system, which we call Djenne, that can aggregate the computational power of the distributed devices because the increased capacity of these devices does allow for substantial work closer to the devices. The challenge that we overcome with Djenne is dependability: how can we cope with failures and the dynamics of wireless network links in such systems? Our design uses the actor model of computation and relies on replicated services to improve reliability and to create opportunities for parallelism that increase task throughput. The key innovations in our work are the use of adaptive mechanisms for rerouting data when system conditions change significantly as well as a holistic recovery approach when computations need to be repeated in the distributed system. Via experimental evaluation, we find that Djenne can improve throughput by 30% to 190% for different use cases while ensuring resilience in the face of intermittent failures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".