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Intermittent computing is gaining traction in application domains such as Energy Harvesting Devices (EHDs) that experience arbitrary power failures during program execution. To make progress, programs require system support to checkpoint state and re-execute after power failure by restoring the last saved state. This re-execution should be correct, i.e., simulated by a continuously-powered execution. We study the logical underpinning of intermittent computing and model checkpoint, crash, restore, and re-execution operations as computation on Crash types. We draw inspiration from adjoint logic and define Crash types by introducing two adjoint modality operators to model persistent and transient memory values of partial (re-)executions and the transitions between them caused by checkpoints and restoration. We define a Crash type system for a core calculus. We prove the correctness of intermittent systems by defining a novel logical relation for Crash types.
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Optimal Checkpointing Strategy for Real-time Systems with Both Logical and Timing Correctness
Real-time systems are susceptible to adversarial factors such as faults and attacks, leading to severe consequences. This paper presents an optimal checkpoint scheme to bolster fault resilience in real-time systems, addressing both logical consistency and timing correctness. First, we partition message-passing processes into a directed acyclic graph (DAG) based on their dependencies, ensuring checkpoint logical consistency. Then, we identify the DAG’s critical path, representing the longest sequential path, and analyze the optimal checkpoint strategy along this path to minimize overall execution time, including checkpointing overhead. Upon fault detection, the system rolls back to the nearest valid checkpoints for recovery. Our algorithm derives the optimal checkpoint count and intervals, and we evaluate its performance through extensive simulations and a case study. Results show a 99.97% and 67.86% reduction in execution time compared to checkpoint-free systems in simulations and the case study, respectively. Moreover, our proposed strategy outperforms prior work and baseline methods, increasing deadline achievement rates by 31.41% and 2.92% for small-scale tasks and 78.53% and 4.15% for large-scale tasks.
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- Award ID(s):
- 2143256
- PAR ID:
- 10424706
- Date Published:
- Journal Name:
- ACM Transactions on Embedded Computing Systems
- ISSN:
- 1539-9087
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3603172
