efficient dissemination of personalized information using content-based multicast (cbm)

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

An SAIC Company

Rahul Shah* Ravi Jain* Farooq Anjum

Dept. Computer Science Autonomous Comm. Lab Applied ResearchRutgers University NTT DoCoMo USA Labs [email protected] [email protected] [email protected]

*Work performed while at Applied Research, Telcordia

Ravi Jain / 20-Jun-02 / 2

Outline

Motivation and background Problem definition Simulation results Concluding remarks


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

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

• 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

ContentSource

= Active Filter1 2 3 4 5 6 7 8Items

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

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

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

Users

Items desired


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

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 problemOptimal centralized algorithm

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(x) = Traffic required at node x

• Execution time = O(k n2) n = number of nodes in tree• Time complexity calculated using Tamir (1996)

Model of multicast tree at source

Child of Lowest filteringancestor, pf(p)

f(r)f(l)

T(v, i, p)

i filters, max

j filters

i - (j -1) filters

Node v

f(p)


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

k filters

01,000

2,0003,000

4,0005,000

6,0007,000

8,0009,000

0 0.2 0.4 0.6 0.8 1

Subscription probability, p

Op

tim

um

To

tal T

raff

ic

(mes

sag

es)

0

3

5

10

15

20

30

63


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

q

0

3,000

6,000

9,000

0 16 32 48 64

Num ber of filters , k

Op

tim

um

To

tal

Tra

ffic

(m

essa

ges

)

1

0.99

0.97

0.95

0.9

0.8

0.7

Effect of locality, q


Bandwidth minimization problemHeuristic centralized algorithm

Importance of node v: I(v) = (f(v) - f(l)) z(l) + (f(v) - f(r)) z(r), wherez(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

z(l)affectededges

z(r)affectededges

f(v)

f(r)f(l)k filters, max

Node v

Node importance, I = amount by which total

traffic changes by placing a filter there

Execution time = O(n)


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


k = 15, p = 0.3Algorithm used

5,220

5,230

5,240

5,250

5,260

5,270

5,280

5,290

5,300

- 10 20 30 40 50 60

Trial instance (time unit)

To

tal

traf

fic

(mes

sag

es) opt

heu

init

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

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

p = 0.3 + q p = 0.3 - q


User mobility motivates filter mobility

Subscription skew, q

0

10

20

30

40

0 10 20 30 40

Number of filters

Red

uct

ion

in

tra

ffic

wit

h f

ilte

r m

ob

ilit

y (%

)

0.2

0.1

0


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

efficient dissemination of personalized information using content-based multicast (cbm)

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