d-RADAR

1. d-RADAR A Dynamic Neighbourhood Discovery Protocol for Active Overlay Networks

2. Introduction: Overlays of Active Routers Why overlays for active networks ? • We’ll only have some active routers • Capsules sent to non-active routers are lost A • Link active routers with tunnels What properties do we wish ? • Routes containing enough active hops to be “interesting” for multicast, transcoding, local retransmission, information merging, etc. • Neighbours reachable without crossing another active node • Active routes close to the IP route B C  Clearly, we do not want a full mesh.

3. Challenges in Neighbours Discovery X S Y X X X Why are (active) overlays different ? • Many neighbours per interface • Stopping at first neighbour may be too early • Rather try to stop when we have “enough” neighbours • Keep discovery cost low • Avoid to scan entire domain when possible  How far we should search depends on how dense active nodes are • React to topology changes quickly • So that we can improve service quality • Because active nodes may start/stop Execution Environment at anytime Y S  The protocol should detect how far it must scan  we’ll have to check neighbours periodically

4. Existing Discovery Techniques How do existing overlay work ? • Manually configured neighbours • This is the *BONE way • Unscalable and too static • Well known Rendezvous point • Common in peer-to-peer networks • All the active routers will have to contact it • Network cannot repair when rendezvous is down • DNS-based registry • modifying the DNS requires manual intervention • Not well-suited when join/leave are frequent We want something plug’n’play We need something decentralized

5. How does it work ? AYA AYA AYA AYA AYA IP: SX IP: YS • « Are You Active ? » Capsules • sort of active “ICMP ECHO” • First active node on the path will reply. • Plain IP nodes forward or drop them • Used in a Ring-Search approach • Using the content of IP routing table and sort it by cost • AYA replies update the search horizon (threshold) • Thrsh:=Thrsh* on replies • Thrsh+=#unreplied when ring done  adapts the search horizon to the active node density. X AYA: Source=STarget=X +1 * * Y Nghb=Y * +1 +1 S An active node that “hides” more targets is more likely to stop the search.

6. Neighbours Come and Go … Stopping the EE on X May occur even if the router is up: does not imply change at IP level Previously hidden targets can now join S’s neighbourhood Threshold must be re-computed: Ti := Ti-1 alpha ^ ai + (ni-ai), alpha<1 Starting an EE Some targets will become hidden (when scanned again) Threshold must be adjusted too IP Routes Change Drop state known about route’s destination then add the new state. To do: Illustrer la disparition d’un nœud et des nœuds cachés qui peuvent être atteints. hidden A B Y D C To do: Illustrer la correction du seuil (modification de ai, recalcul de Ti etc. X T2=T1*a+1 Z T2=T1*1+2 T1=T0+1 To do: Illustrer la réapparition d’un nœud. La partie « quid des nœuds qui deviennent down » sera traitée plus loin. N Scanner queue

7. Keeping Information Up to Date Expiration date for each neighbour Try to refresh the information by scanning when entry expires Refreshes become less frequent as the entry’s age increases Minimal refresh period is enforced. Tag non-responding neighbours They’ve become down neighbours Reset their age and keep scanning They may be unable to announce they’re back (asymmetric neighbour-hood) Motivations: new information is more likely to be temporary, old, periodically-confirmed information is more likely to be permanent AYA To do: Illustrer le cas d’un nœud qui est trop loin pour annoncer qu’il est de retour (fig3) (cause: asymétrie de la relation de voisinage) Y X Ngh(N)={Y} Down(N)={X} Ngh(N)={X,Y} N • Recovers quicker from transitory failures • “recognize” permanent/temp. neighbours

8. D-RADAR: Simulation Results Bootstrap resource consumption • One AN sends less AYA on dense networks than on scarce ones • The overall resource consumption is still lower on scarce networks Refresh Traffic • Less dependent of the AN density • Mainly concentrated on routes between AN (5 to 20Kbps) 49% of AN out of 60 7% of AN out of 60

9. Simulating Dynamic Topologies Node fails: • Discovered by AYA refreshes • Almost no traffic penalty Route updates: • AYA “burst” on new routes • Unused node  no penalty Failure on an active trunk Failure of an “unused” node

10. Required Environment 12.34.56.0  gw0:eth0 $10 What do we need ? • Writing to the overlay’s neighbours list. • Capsules that can be grabbed. • “Previous hop” in the capsules • Reading the IP routing table (+notifications) • Route cost in the IP table • “Last IP before reaching target X” information (optimization) Radar Active Routing EE NodeOS IP Last hop=12.34.50.1

11. Conclusions & Future Work • We provided a neighbourhood discovery for ANTS which is able to adapt to varying topology conditions • Adaptive threshold algorithm allows plug’n’play behaviour (no topology-specific settings) • Detects failure with low traffic, “repair” cost around 20% of a bootstrap recovery. Cost on mobile/ad hoc topologies yet to be studied • Techniques used in peer-to-peer network could be interesting to connect hosts to a RADAR core Thank you for your attention …

12. Questions ?