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Large Scale Q-in-Q

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Mick Seaman 2. Perceived problem ... Mick Seaman 5. Making the best of this ... Mick Seaman 7. Optimizing learning on shared VLANs ... – PowerPoint PPT presentation

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Title: Large Scale Q-in-Q


1
Large Scale Q-in-Q
1. Scaling address learning mick_seaman_at_ieee.or
g
2
Perceived problem
  • As a Provider Bridged Network grows and attracts
    more customers it will contain more Provider
    Bridges that learn the addresses of each customer
    so the total amount of address learning
  • fn(of customers x of Provider Bridges)
  • fn(of customers2)
  • fn(value provided2)

3
Preliminary answer
  • Dont learn on point-to-point Service Instances
  • But what about Services Instances with more than
    2 points of attachment?

4
Better answer
  • Dont learn unless the learning will affect where
    frames are forwarded e.g. dont learn when
  • Receiving port is attached to point-to-point LAN
  • There are less than two ports on this bridge that
    are in the (egress) Member set for this VLAN

5
Making the best of this
  • Use GVRP to scope the extent of the Service VLAN
    so extent of VLAN is minimal over its Spanning
    Tree but extent of VLAN autoconfigures after
    active topology reconfiguration
  • For S-VLAN with 3 customer attachments learning
    only required in 1 provider bridge
  • For S-VLAN with n customer attachments learning
    only required in (n-2) provider bridges at most
  • Minimizes database flushing impact after change

6
Shared VLANs
  • Some stations transmit on A, receive on B
  • Others receive on A, transmit on B
  • Allows multiple subscribers to be served using
    one customer network

7
Optimizing learning on shared VLANs
  • Only learn addresses from frames on VLAN A if
    VLAN B has more than one way for a frame that has
    entered a bridge to leave

8
LARGE scale with shared VLANs
Routers/servers here
Addresses of subscribers (below) only learnt in
this bridge and below
Subscribers attached to these bridges
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