Keep Calm but Log for Trouble: Coordination-Free Fault-Tolerance for In-Switch Applications
Bibliographic record
Abstract
In-network computing (INC) offloads parts of the functionality of a distributed system to programmable switches. Once we move the computation into the network, failures may cause loss of essential information and disrupt the operation of these systems. To ensure high availability in the event of switch failures, the current state-of-the-art replicates state across multiple INCs. They achieve this by using state-machine replication, ensuring that INC replicas are consistent by coordinating the replication between multiple INC nodes. In this paper, we demonstrate that decoupling the consistency guarantees from the replication reduces the overhead of INC fault tolerance. We present RESIST, a system for building fault-tolerant INC using asynchronous replication and replay-based recovery. We propose new techniques for logging information and different replay techniques to restore INC systems according to consistency semantics. Furthermore, we apply RESIST techniques to existing INC functionalities, including event synchronization for distributed simulations and aggregation for distributed training. Our prototype of RESIST enables fault tolerance for INC applications on BMv2 and in a testbed with Tofino ASICs. Experiments show that the system provides fault tolerance with negligible overhead for non-failure scenarios and recovers from failures in less than 0.2s.
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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 | 0.000 | 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 | 0.003 | 0.001 |
| 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".