Algorithmic Fault Impact Evaluation in Mobile Computation Offloading
Bibliographic record
Abstract
Offloading computation-intensive applications from a mobile device to remote servers can improve the response time of the mobile device as well as its battery life. However, the benefits of computation offloading can be affected by faults in computation resources and network links, making resource management and task allocation even more challenging when fault tolerance is a consideration. While several mechanisms exist for protecting against such faults, fault-tolerance mechanisms typically introduce overheads, creating a challenge for efficient resource management in computation offloading. To reduce the overheads of fault-tolerance mechanisms in computation offloading, these mechanisms can be selectively applied to parts of the application with the highest fault impact. This necessitates the identification of such parts, which is a complex and time-consuming undertaking. In this paper, we propose a novel algorithmic approach tailored for cloud resource management that can evaluate the fault impact of tasks of an application, eliminating the need for time-consuming fault injection simulations. This approach enables more rapid analysis of an existing scenario, as well as more efficient exploration of alternative resource allocations. The proposed approach is evaluated on both an example workflow graph and a real face recognition application, and the results are verified against fault injection simulations. We also present a case study and show how the insights obtained from this analysis can be used to selectively apply fault-tolerance mechanisms to tasks with the highest fault impact within an application, to better manage and utilize additional cloud resources when designing for improved fault tolerance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".