| title | date | lastmod |
|---|---|---|
Failure Detectors |
2023-01-31 |
2023-03-08 |
- Fail-stop: can reliably detect failure (achievable in synchronous model)
- Fail-noisy: can eventually detect failure (achievable in partially synchronous model)
- Fail-silent: cannot detect between a crash or omission failure (asynchronous model)
No false negatives: all failed processes are suspected Asynchrony: suspect every node to achieve completeness
No false positives: correct processes are not suspected Asynchrony: suspect 0 nodes to achieve accuracy
No correct process is ever suspected
There exists a correct process P which is never suspected by any process
After some time, strong accuracy achieved, prior to this, any behaviour possible.
- This does not satisfy weak accuracy, as before achieving strong accuracy, any behaviour allowed
After some time, weak accuracy achieved, prior to this, any behaviour possible.
- Timeout T is increased at q
- Eventually system becomes synchronous, and T becomes larger than the unknown bound δ (T>γ+δ)
- q will receive HB on time, and never suspect p again
We want all processes to detect a single and same correct process. To do so, we need to define the set of failed processes (using a FD)
- If strong accuracy, no one is ever inaccurate, reduction never spreads inaccurate Susp
- If weak accuracy, everyone is accurate about at least one process p, no one spreads inaccurate information about p
Implement S using
- Strong completeness
$\equiv$ weak completeness: if we suspect everyone (except the leader which we know is correct), every process suspects all incorrect processes - Eventual weak accuracy: if we only trust the leader, there exists 1 correct process not suspected
