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

1. 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

2. Outline • Motivation and background • Problem definition • Simulation results • Concluding remarks

3. 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

4. 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

5. = 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. User mobility motivates filter mobility

16. 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