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An SAIC Company. Efficient Dissemination of Personalized Information Using Content-Based Multicast (CBM). Rahul Shah* Ravi Jain* Farooq Anjum Dept. Computer Science Autonomous Comm. Lab Applied Research Rutgers University NTT DoCoMo USA Labs Telcordia

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Efficient Dissemination of Personalized Information Using Content-Based Multicast (CBM)


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efficient dissemination of personalized information using content based multicast cbm

An SAIC Company

Efficient Dissemination of Personalized Information Using Content-Based Multicast (CBM)

Rahul Shah* Ravi Jain* Farooq Anjum

Dept. Computer Science Autonomous Comm. Lab Applied Research

Rutgers University NTT DoCoMo USA Labs Telcordia

sharahul@paul.rutgers.edujain@docomolabs-usa.comfanjum@telcordia.com

*Work performed while at Applied Research, Telcordia

outline
Outline
  • Motivation and background
  • Problem definition
  • Simulation results
  • Concluding remarks
mobile filters for efficient personalized information delivery
Mobile Filters for Efficient Personalized Information Delivery
  • Users want targeted, personalized information, particularly
    • as the amount and diversity of information increases,
    • the capabilities of end devices are limited and resources are scarce
  • Applications like personalized information delivery to large numbers of users rely on multicast to conserve resources
  • Traditional network multicast (e.g. IP multicast)
    • does not consider the content or semantics of the information sent
    • Management difficult as number of groups increase
  • Content-Based Multicast (CBM) filters the information being sent down the multicast tree in accordance with the interests of the recipients
  • Problem: how to place software information filters in response to
    • the location and interests of the users, and how these change
    • the additional cost and complexity of the filters
related work
Related work
  • Multicast
    • Application layer multicast
      • Assumes only unicast at the IP layer, while CBM assumes a multicast tree (either at the IP or the application layer)
      • Examples: Francis, Yoid, 2000; Chu et al., End System Multicast, Sigmetrics 2000; Chawathe et al., Scattercast, 2000
    • Publish-subscribe systems
      • Many-many distribution with matching done by brokers in the network
      • In CBM the brokers form the underlying multicast tree
      • Examples: Aguilera, 1998; Banavar, 1998; Carzaniga, 1998
    • Modifications to IP multicast
      • Opyrchal, Minimizing number of multicast groups, Middleware 2000
      • Wen et al., Use active network approaches, OpenArch 2001
  • Theoretical work
    • Classical k-median and facility location problems
multicast filtering example

= Active Filter

Content

Source

3

6

1

2

5

8

4

7

Items

1, 3, 4, 5, 6, 7, 8

1, 3, 5, 6, 7, 8

1, 3, 5

3, 6, 7, 8

1, 3, 5, 8

4, 6, 7, 8

Users

3, 5

1, 8

3, 8

6, 7, 8

4

3, 6

7,8

1, 5

Items desired

1, 3

Multicast filtering example
  • Without filters, all 8 items are sent on all 15 links = 120 traffic units
  • With filters at all internal nodes, traffic = 47 units
  • With filters at 3 internal nodes, traffic = 63 units
mobile code problem definition
Mobile code problem definition
  • Problem 1: Bandwidth optimization problem
    • Criterion: Find optimal placement to minimize total bandwidth
    • Cost model: k-Filters: Allow at most k filters to be used
  • Problem 2: Delay optimization problem
    • Criterion: Find optimal placement to minimize mean delivery delay
    • Cost model: Delay:
      • Each filter adds a delay D for processing
      • The reduction in link utilization also results in reduction in link delay:
  • Optimal placement changes as users move or change interests
    • the filtering code should or could be mobile and
    • the placement algorithm should be fast
  • Results:
    • optimal centralized off-line algorithm for bandwidth optimization. Time = O(k n2)
    • optimal centralized off-line algorithm for delay optimization. Time = O(n2)
    • Two centralized O(n) heuristics that restrict filter moves
    • Evaluation using simulations
filtering algorithm framework
Filtering algorithm framework
  • For simplicity, we assume the following framework
    • 1: The multicast tree has previously been constructed and is known
    • 2: Filters can be placed at all internal nodes of the multicast tree
        • If not, simply consider the subtree where filters are permitted
    • 3: Subscriptions propagate from the users to the source
      • There is a simple list of information items that users can request
      • Subscription changes are batched at the source
        • At every batch (time slice) x% of the users change subscription
    • 4. The source calculates filter placements
    • 5: The source dispatches filters to the (new) placement
      • Currently we ignore signaling costs of subscriptions and filter movement because negligible for the applications considered (news clips, video clips, music, etc)
      • Alternatively could consider that filters are available at all nodes and are only activated/deactivated by signaling messages
bandwidth minimization problem optimal centralized algorithm
Bandwidth minimization problemOptimal centralized algorithm

f(p)

Child of

Lowest filtering

ancestor, p

Model of multicast tree at source

  • Dynamic programming recurrence relations
    • Traffic in the subtree rooted at v, with a filter at v:

T(v, i, p) = f(l) + f(r) + min[ j: 0  j  i: T(l, j, l) + T(r, i - j - 1, r) ]

    • Traffic with no filter at v:

S(v, i, p) = 2 f(p) + min[ j: 0  j  i: T(l, j, p) + T(r, i - j, p) ]

    • Traffic at a leaf node v: T(v, i, p) = S(v, i, p) = 0
    • Minimum traffic is min[ T(v, k, p), S(v, k, p) ]

f(p)

  • f(x) = Traffic required at node x
  • Execution time = O(k n2)
  • n = number of nodes in tree
  • Time complexity calculated
  • using Tamir (1996)

T(v, i, p)

Node v

i filters,

max

f(l)

f(r)

j

filters

i - (j -1)

filters

simulation results filters can be very effective
Simulation results: Filters can be very effective
  • Seven-level complete binary tree (n = 127), with 64 leaves
  • m = 64 messages
  • Uniform subscription: p(i, j) = Prob [ User i subscribes to message j ] = p
interest locality increases filtering benefits

Locality model: P(i, j) = 1/N if i = j

    • = qr /N else, where r = LCA(i, j)
    • q is a skew parameter inversely proportional to locality
Interest Locality increases filtering benefits
bandwidth minimization problem heuristic centralized algorithm

f(v)

Node v

k filters,

max

f(l)

f(r)

z(l)

affected

edges

z(r)

affected

edges

Bandwidth minimization problemHeuristic centralized algorithm
  • Node importance, I

= amount by which total

traffic changes by

placing a filter there

  • Execution time = O(n)
  • Importance of node v:

I(v) = (f(v) - f(l)) z(l) + (f(v) - f(r)) z(r), where

z(x) = 1, if x has a filter

1 + z(left-child of x) + z(right-child of x), otherwise

  • z(x) is number of edges in the subtree rooted at x affected by a filter at x
centralized heuristic
Centralized heuristic
  • Subscriptions propagate up to the source, which
    • calculates the required flow amount at each edge and the Importance value of each node
    • tries the Importance Flip
      • Imax(v) = max[ v: v does not have a filter: I(v)]
      • Imin(u) = min[ u: u has a filter, I(u)]
      • If Imax(v) > Imin(u), move the filter from u to v
        • If the most Important non-filtering node is more important than the least Important filtering node, swap the filter location
    • otherwise, tries the Parent-child flip
    • is allowed to make at most one filter move
  • The source dispatches one new filter, or a move instruction to one existing filter
code mobility is not useful with uniform subscriptions and static users
Code mobility is not useful with uniform subscriptions and static users
  • opt = optimal placement at each trial
  • heu = heuristic re-run at each trial
  • Init = initial placement, kept unchanged
mobility model

p = 0.3 + q

p = 0.3 - q

Mobility model
  • User mobility: Users gradually move from the left subtree to the right subtree
    • Subscription skew, q
    • At t = 0, users in left subtree have

p = 0.3 + q, users in right p = 0.3 - q

    • At t = i, swap probabilities of user i in left

subtree with user i in right subtree

further work
Further work
  • Theoretical improvements:
    • More efficient algorithms
      • Achieves O(n logn) time complexity
  • Prototype and obtain actual bandwidth costs and delays for filter movement using Aglets technology
  • A distributed filtering algorithm, where the filters are agents that coordinate with minimal involvement of the source
    • How to avoid thrashing and loops
    • How to ensure semi-autonomous agent movements do not degrade performance
  • Investigate different application domains