Traffic grooming in WDM Networks

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Traffic grooming in WDM Networks

• Dynamic Traffic Grooming in WDM Mesh Networks
  Using a Novel Graph Model by
• Hongyue Zhu, Hui Zang, Keyao Zhu, and
  Biswanath Mukherjee

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What is Traffic Grooming?

• When low speed traffic streams are multiplexed
  and switched onto high-speed light paths, we say
  traffic is groomed.
• Grooming is mainly done to reduce the no of Add
  Drop Multiplexers (ADM) required. As they are
  major contributors to the total cost.

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Motivation for Traffic Grooming

• Suppose that each wavelength is used to support
  anOC-48 ring, and that the traffic requirement is
  for eight OC-3 circuits between each pair of
  nodes. In this example we have six node pairs,
  and the total traffic load is equal to 48 OC-3s
  or equivalently three OC-48 rings. In the next
  slide 2 possible assignments are shown.

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Motivation for Traffic Grooming(cond..)

• Thus we can see that by careful selection of
  wavelengths passing through a node we can reduce
  the required no of ADMS.
• Application of RAW alone does not imply that the
  solution selected is optimal in no of ADMS
  required. Consider the example on the next slide.

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• As shown RAW 1 requires only 2 wavelengths and 15
  AMDS. While RAW 2 requires 3 wavelengths , but
  consume only 9 AMDS.
• Generally a traffic grooming problem can be
  formulated as an ILP. But as the network size
  grows the no of equations and variables increase
  explosively.

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Novel Graph Model

• Auxiliary graph Captures various network
  constrains, like no of transceivers at each node,
  no of wavelengths on each fiber-link,
  wavelength-conversion capabilities of each node
  etc (will be discussed in details)
• Dynamic traffic grooming Algorithm This is a
  route computing algorithm, which take the weight
  function into account. Thus by dynamically
  adjusting the weights on the edges, one could
  evolve from one grooming policy to another, as
  demand changes.

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Construction of an Auxiliary Graph

• Consider a network of 3 nodes
• Each link has two wavelengths
• All nodes are assumed to have grooming
  functionality
• Node 0 has full wavelength-conversion
• Node 1 has no wavelength-conversion
• Node 2 has limited wavelength-conversion
  capability (i.e. wavelength 1 can be converted to
  wavelength 2)

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Construction of an Auxiliary Graph(cond..)

• Auxiliary graph is a layered graph with w2
  layers, where w no of wavelengths
• W1 layer is called the Light path Layer
• W2 layer is called the Access layer.
• Each node layer has 2 vertices input (I) and
  output (o).

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Construction of an Auxiliary Graph(cond..)
Meaning of Edges

• Wavelength Bypass Edges (WBE). There is an edge
  from the input port to the output port on each
  wavelength layer l at node i, denoted as WBE (i,
  l).
• Grooming Edges (GrmE). There is an edge from the
  input port to the output port on access layer at
  node I if node i has grooming capability, denoted
  as GrmE (i).
• Mux Edges (MuxE). There is an edge from the
  output port on the access layer to the output
  port on the lightpath layer at each node, denoted
  as MuxE(i).

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Construction of an Auxiliary Graph(cond..)
Meaning of Edges

• Demux Edges (DmxE). There is an edge from the
  input port on the lightpath layer to the input
  port on the access layer at each node, denoted as
  DmxE (i).
• Transmitter Edges (TxE). There is an edge from
  the output port on the access layer to the output
  port on wavelength layer l, denoted as TxE(i, l),
  if there are transmitters available on wavelength
  ?i at node i.

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Construction of an Auxiliary Graph(cond..)
Meaning of Edges

• Receiver Edges (RxE). There is an edge from the
  input port on wavelength layer l to the input
  port on the access layer, denoted as RxE(i, l),
  if there are receivers available on wavelength ?i
  at node i.
• Converter Edges (CvtE). There is an edge from the
  input port on wavelength layer l1 to the output
  port on wavelength layer l2 at node i, denoted as
  CvtE(i, l1, l2), if wavelength l1 can be
  converted to wavelength l2 at node i.

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Construction of an Auxiliary Graph(cond..)
Meaning of Edges

• Wavelength-Link Edges (WLE). There is an edge
  from the output port on wavelength layer l at
  node i to the input port on wavelength layer l at
  node j, denoted as WLE(i, j, l), if there is a
  physical link from node i to node j and
  wavelength ?l on this link is not used.
• Lightpath Edges (LPE). There is an edge from the
  output port on the lightpath layer at node i to
  the input port on the lightpath layer at node j,
  denoted as LPE(i, j), if there is a lightpath
  from node i to node j.

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Construction of an Auxiliary Graph(cond..)

• Each edge is associated with the tuple P(c,w).
• For wavelength-link edges c capacity of the
  corresponding wavelength on the corresponding
  link.
• For lightpath edges c residual capacity of
  corresponding lightpath.
• For all other type of edges c infinity.
• Weight w reflect cost of element.
• Weights can be fixed of adjusted in accordance to
  network state
• Fixed weight ? Fixed grooming policy
• Variable weight ? Adaptive grooming policy

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Auxiliary Graph
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Dynamic traffic grooming Algorithm

• Inputs
• Initial network state
• Set of traffic demands represented as
• T (s, d, g, m). s source, d destination, g
  granularity of traffic and m amount of traffic
  in g units.

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Algorithm steps

• Initialize Construct auxiliary graph.
• When request T arrives
• 1 Compute the shortest path p from the output
  port on the access layer of the source to the
  input port on the access layer of the destination
  of T on graph G, ignoring the edges whose
  capacities are less than the requirement of the
  request. If such a path does not exist, block the
  traffic demand otherwise, continue with the
  following steps.

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Algorithm steps

• 2 If p contains wavelength-link edges, set up one
  or more lightpaths going through the
  corresponding wavelength-links.
• 3 Route T along the pre-existing lightpaths in p
  and/or lightpaths newly set up according to p.
• 4 Update graph G as follows

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Algorithm steps

• For each newly setup lightpath, a lightpath edge
  from the output port of the starting node of the
  lightpath to the input port of the ending node of
  the lightpath is added on the lightpath layer.
• The wavelength-link edges used by the lightpath
  are removed from the corresponding wavelength
  layers.

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Algorithm steps

• If there is no more transmitter/receiver
  available at node i on wavelength ?l , the
  corresponding transmitter/receiver edge will be
  removed from G, i.e., this node cannot
  source/sink a lightpath on wavelength ?l any more
  and can only be bypassed by a lightpath.
• If there is no more wavelength converter which
  can convert wavelength l1 to wavelength l2
  available at node i, the converter edge will be
  removed from G.
• Update tuple P(c,w)

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Algorithm steps

• 5 If connection removed
• A Remove the traffic from network.
• B Tear down all the lightpaths
• C Update graph G by applying reverse of update
  methods used in step 4 above.

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Example

• Assume
• Capacity of each wavelength OC-48
• Each node has grooming capability and two tunable
  transceivers.
• First connection request
• T(1, 0, OC-12, 2)
• Path found
• TXE(1,1) WLE(1,0,1) and RXE(0,1)
• LPE(0,1) 24

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Example
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Example

• Another request
• T(2,0,OC-12,1)
• Path found
• Case1 TxE(2,2), WLE(2,1,2), WBE(1,2),
  WLE(1,0,2), and RxE(0,2)
• LPE(2,0) 36 LPE(1,0) 24
• Case2 TxE(2,1), WLE(2,1,1), RxE(1,1), GrmE(1),
  MuxE(1), LPE(1,0), and DmxE(0)
• LPE(2,1) 36 AND LPE(1,0) 12

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case1
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Case 2
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Grooming Operations

• Op1Route the traffic onto an existing lightpath
  directly connecting the source s and the
  destination d.
• Op2 Route the traffic through multiple existing
  lightpaths.
• Op3 Set up a new lightpath directly between the
  source s and the destination d and route the
  traffic onto this lightpath. Using this
  operation, we set up only one lightpath if the
  amount of the traffic is less than or equal to
  the capacity of the lightpath.

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Grooming Operations

• Op4 Set up one or more lightpaths that do not
  directly connect source s and destination d, and
  route the traffic onto these lightpaths and/or
  some existing lightpaths. Using this operation,
  we need to set up at least one new lightpath.
  However, since some existing lightpaths may be
  utilized, the number of wavelength-links used to
  set up the new lightpaths could be less than the
  number of wavelength-links needed to set up a
  lightpath directly connecting source s and
  destination d.

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Grooming Policies

• By combining various grooming operations in
  different priority order , we can achieve
  different grooming policies
• Minimize the Number of Traffic Hops on the
  Virtual Topology (MinTHV) This policy chooses
  the route with the fewest lightpaths for a
  connection.
• Minimize the Number of Traffic Hops on the
  Physical Topology (MinTHP) We compare the
  number of wavelength-links used by all the four
  operations and choose the one with the fewest
  wavelength-links.

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Grooming Policies

• 3. Minimize the Number of Lightpaths (MinLP)
  This policy is similar to MinTHV but it tries to
  set up the minimal number of new lightpaths to
  carry the traffic.
• 4. Minimize the Number of Wavelength-Links
  (MinWL) This policy is similar to MinTHP but it
  tries to consume the minimum number of extra
  wavelength-links, i.e., wavelength-links not
  being used by any lightpaths for now, to carry
  the traffic

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Dominant edge

• if a path p1 in the graph contains more of this
  kind of edges than another path p2, then the
  weight of p1 is always larger than that of p2.
  Here, the weight of a path is the summation of
  the weights of the edges it traverses.
• Example
• To achieve MinTHV, we just need to make GrmEs the
  dominant edges.
• To achieve MinLP, we should make TxEs and RxEs
  the dominant edges.
• To achieve MinWL, WLEs should be the dominant
  edges

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Results
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Results
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Adaptive grooming policy

• Since MinTHV performs best when transceivers are
  the more constrained resources and MinTHP gives
  the best results when wavelength-links become
  more scarce resources, Adaptive Grooming Policy
  (AGP) take advantages of both these policies and
  performs well over all network conditions.

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Adaptive grooming policy

• ratio of the number of unused wavelength-links in
  the network to the total number of available
  transceivers at all nodes as an indicator of the
  network state. If the ratio is larger than the
  set threshold d1 then MinTHV will be used and if
  the ratio is less that the set threshold d2 then
  MinTHP will be used. If ratio is in between then
  the policy is not changed.

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Adaptive grooming policy
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Adaptive grooming policy
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Conclusion

• The new model takes various constrains into
  account and can achieve various objectives by
  using different grooming policies. The ability to
  adjust grooming policy by changing the weights of
  the edges makes this model very suitable for
  dynamic traffic grooming.