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Marco Realacci 2024-10-11 17:44:32 +02:00
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Challenges
- Atomicity
- you write to the queue first
- you write to the db first
- we need a transaction!
- Idempotence
- we need locking mechanism on the queue, to avoid multiple process to process the same element
- solution: replay protection (lastUpdate in the db), but still I have the problem of multiple process reading the same element
## Data replication
- Waiting for all replicas
- definitely slower
- safer for the data
- Waiting only for the primary replica
- faster but...
- replica may fail
- not getting always the same value
- ok for maybe Instagram feed, but not for e-mails
## Paxos
Paxos overview:
- partecipnats are: Writers & Replicas
- 2 phase protocol:
- **prepare** reserves right to supply value, and robs other
- **accepts** supplies the value **v**
- replicas are stateful
- communication is ordered by "proposal number" **n**
- uses "viral propagation"
#### Algorithm
- Prepare phase
- Success if replica hasn't prepared a higher proposal
- Fail otherwise
- Success with value if we have accepted another value (viral property)
Majority succeded -> Go to Accept Phase
- Accept phase
- Success only if replica hasn't Prepared a higher proposal (same is ok)
### Mesh
- full mesh
- subsetting: not every node connected to every other node: in case of a query of death, only connected nodes will fail
- proxying: adding a layer of proxies (load balancers), that use a lookup service to route the request
### Strong stickiness
Some applications like chat, online game servers, video calls ecc. need a very strong stickiness.
In these cases, we may not want to use consistent hashing. Maybe we can use it to pick a server and then stick with it.
Also, it can happen that some users are not able to reach a specific server, but other does.
Using leases might be a solution.
- Request from Alice
- lookup server response is cached
- we have a failure
- we call Set to change the server, but the call stalls until the lease expires. Also, the lookup server might be able to still reach the server and renew the lease

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Biometric Systems/final notes/2. Performance indexes.md
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@ -98,3 +98,39 @@ Of course templates should be different, not computed i.e. by frames of the same
> - pj does not belong to the gallery (most trivial)
> - pj belongs to an enrolled subject but the probe claimed another identity, not the real one.
What if ERR in two systems is the same, but the curves are different?
We can use ROC curve or DET curve.
For ROC, we can compute the area below the curve and use it as a metric, the higher the better.
#### Possible errors: identificaiton - open set
In an open set identification task, the system determines if the individual's biometric signature matches a signature of someone in the gallery.
The individual **does not make** and identity claim.
- More possible error situations, depending on the matcher and on the threshold
- A problem may occur if the system returns more possible candidates below the threshold. Who is the right one?
> [!PDF|yellow] [[LEZIONE2_Indici_di_prestazione.pdf#page=27&selection=0,8,9,8&color=yellow|LEZIONE2_Indici_di_prestazione, p.27]]
> > Possible errors: identification – open set
>
>
correct detect and identify rate = rate over which the correct individual has the identified score and so is identified correctly.
false alarm rate = rate over which unenrolled users are identified as another user in the db.
We compute that by testing the system with lots of probes belonging to set Pg if enrolled or set Pn if not.
We define
- rango(pj) = the position in the list where the first template for the correct identity is returned
- DIR (at rank k) (Detection and Identication Rate (at rank k)): the probability of correct identification at rank k (the correct subject is returned at position k)
- The rate between the number of individuals correctly recognized at rank k and the number of probes belonging to individuals in PG
- If identification does NOT happen at rank 1, we have a False Reject.
- FRR or more specifically FNIR (False Reject Rate or False Negative Identification Rate): the probability of false reject expressed as 1 - DIR (at rank 1)
- FAR or more specifically FPIR (False Acceptance Rate or False Positive Identification Rate) or False Alarm Rate (Watch List): the probability of false acceptance/alarm
- The rate between the nuber of impostor recognized by error and the total number of impostors in PN
#### Closed set
We don't have thresholds!
The only possible error is that the correct identity does not appear at rank 1.

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