vault backup: 2024-10-11 17:44:32
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6 changed files with 148 additions and 63 deletions
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0
Autonomous Networking/notes/4 WSN2.md
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Autonomous Networking/notes/4 WSN2.md
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Autonomous Networking/slides/4 WSN2.pdf
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Autonomous Networking/slides/4 WSN2.pdf
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BUCA/Queues.md
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BUCA/Queues.md
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@ -0,0 +1,56 @@
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Challenges
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- Atomicity
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- you write to the queue first
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- you write to the db first
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- we need a transaction!
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- Idempotence
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- we need locking mechanism on the queue, to avoid multiple process to process the same element
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- solution: replay protection (lastUpdate in the db), but still I have the problem of multiple process reading the same element
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## Data replication
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- Waiting for all replicas
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- definitely slower
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- safer for the data
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- Waiting only for the primary replica
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- faster but...
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- replica may fail
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- not getting always the same value
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- ok for maybe Instagram feed, but not for e-mails
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## Paxos
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Paxos overview:
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- partecipnats are: Writers & Replicas
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- 2 phase protocol:
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- **prepare** reserves right to supply value, and robs other
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- **accepts** supplies the value **v**
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- replicas are stateful
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- communication is ordered by "proposal number" **n**
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- uses "viral propagation"
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#### Algorithm
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- Prepare phase
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- Success if replica hasn't prepared a higher proposal
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- Fail otherwise
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- Success with value if we have accepted another value (viral property)
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Majority succeded -> Go to Accept Phase
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- Accept phase
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- Success only if replica hasn't Prepared a higher proposal (same is ok)
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### Mesh
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- full mesh
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- subsetting: not every node connected to every other node: in case of a query of death, only connected nodes will fail
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- proxying: adding a layer of proxies (load balancers), that use a lookup service to route the request
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### Strong stickiness
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Some applications like chat, online game servers, video calls ecc. need a very strong stickiness.
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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.
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Also, it can happen that some users are not able to reach a specific server, but other does.
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Using leases might be a solution.
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- Request from Alice
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- lookup server response is cached
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- we have a failure
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- 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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> - pj does not belong to the gallery (most trivial)
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> - pj does not belong to the gallery (most trivial)
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> - pj belongs to an enrolled subject but the probe claimed another identity, not the real one.
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> - pj belongs to an enrolled subject but the probe claimed another identity, not the real one.
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What if ERR in two systems is the same, but the curves are different?
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We can use ROC curve or DET curve.
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For ROC, we can compute the area below the curve and use it as a metric, the higher the better.
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#### Possible errors: identificaiton - open set
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In an open set identification task, the system determines if the individual's biometric signature matches a signature of someone in the gallery.
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The individual **does not make** and identity claim.
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- More possible error situations, depending on the matcher and on the threshold
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- A problem may occur if the system returns more possible candidates below the threshold. Who is the right one?
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> [!PDF|yellow] [[LEZIONE2_Indici_di_prestazione.pdf#page=27&selection=0,8,9,8&color=yellow|LEZIONE2_Indici_di_prestazione, p.27]]
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> > Possible errors: identification – open set
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>
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>
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correct detect and identify rate = rate over which the correct individual has the identified score and so is identified correctly.
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false alarm rate = rate over which unenrolled users are identified as another user in the db.
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We compute that by testing the system with lots of probes belonging to set Pg if enrolled or set Pn if not.
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We define
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- rango(pj) = the position in the list where the first template for the correct identity is returned
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- 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)
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- The rate between the number of individuals correctly recognized at rank k and the number of probes belonging to individuals in PG
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- If identification does NOT happen at rank 1, we have a False Reject.
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- 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)
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- 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
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- The rate between the nuber of impostor recognized by error and the total number of impostors in PN
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#### Closed set
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We don't have thresholds!
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The only possible error is that the correct identity does not appear at rank 1.
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