Fault tolerance in the mobile environment
Bibliographic record
Abstract
In general it is assumed that a parallel program will execute on reliable hardware. A fault tolerant program and underlying infrastructure should be capable of surviving failures such as system crashes and network failures. At the highest level the application should be capable of automatically recovering from a set of faults without any change to the apparent behaviour of the program. The process of checkpointing may be used to allow a program to save its state to persistent storage, abort and restart from the checkpoint. Several fault tolerant MPI implementations are currently in existence, for example MPICH-V is considered to be one of the most complete, featuring checkpointing and message logs to allow aborted processes to be replaced. No matter how sophisticated a fault tolerant system may be, it can never be completely relied upon, as there is always the possibility of a complete system failure. It is one thing to develop fault tolerant applications on high end dedicated clusters and supercomputers, however applying fault tolerance to the realm of mobile parallel computing presents an entire new series of challenges that are inexorably linked with the unpredictable nature of wireless communication systems. Two differing strategies for fault tolerance in the mobile Bluetooth wireless environment will be presented and compared to see which should be adopted over another.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".