Multicast Group Management for Interactive Distributed Applications

(1)

Multicast Group Management for

Interactive Distributed Applications

Carsten Griwodz

[email protected]

September 25, 2008

based on the thesis work of

Knut-Helge Vik, [email protected]

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Group communication management

 Large-scale interactive applications

− one application

− large number of users

− users interact in groups

− group membership changes dynamically

 ^Examples

− Games

• including Second Life

− VoIP Conferencing

• including Skype

− Social Networks

• including some MSN IM

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Game environment

 Typical multiplayer online games today

− Central server-based

− Experience high latency

 Physical world and virtual world locality are unrelated

Real-World Proximity

Virtual World Proximity

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Game environment

 Anarchy Online servers are located in the US

− Here most action is in Europe

− At other times of day the center of action shifts

North America

Europe

Asia

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Group communication management

 Large-scale interactive applications

− Users interact in groups

− Communication demands vary even within an application

• low latency demands

• high bandwidth demands

• frequent group membership changes

• consistency

− Build overlay routing

• with a small diameter

• with degree limitations

• using algorithms with low execution times

• with stable reconfigurations

− Data acquisition in the Internet

− Also: comparison with meshes

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Real-World Proximity

Virtual World Proximity

Motivation – MMOGs

Large number

of users

Long distances

Group players

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Motivation

 Reduce (or match) problem to graph theory

− Clients are vertices V, links are edges E. Edge cost may be latency. Graph G=(V,E,c).

− Events are sent by all to all, therefore G is undirected. (Ex: position updates)

− Multicast events within a shared tree. Create a group tree T from group graph G.

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Motivation

 Optimizations – Goal: Reduce latency, and make it cheap.

− Given a graph G=(V,E,c). Create a shared tree T.

− Optimizations in trees: Total cost, diameter, node degree (stress).

− Optimizations of algorithms. Ex: Reduce size of G and reduce execution time.

Proxy Proxy

Proxy

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Research - Group Dynamics

 Dynamic membership – nodes join and leave the multicast tree dynamically

− Must insert and remove nodes online

 Needs algorithm to reconfigure the tree

 Steiner tree heuristics

− Shortest path heuristics (SPH) - O(pn

²

)

− Distance network heuristics (DNH) - O(pn

²

)

− Average distance heuristics (ADH) - O(n

³

)

 Bounded tree heuristics

− Degree-limited shortest path tree (dl-SPT) - O(n

²

)

− mddl-OTTC - heuristic for minimum diameter degree-limited spanning

tree problem - O(n

³

)

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Research - Optimization



Application layer graphs are fully meshed

− Ex: |V|=100, |E| = 4950 edges. Edges in tree: |E_T|= |V| - 1 = 99 (0.02 % of the edges)

− Tree algorithms build trees using graphs



Graph optimization techniques

− Reduce reconfiguration sets – reconfigure smaller parts of a group



Core selection heuristics:

− Include stronger nodes in the input graph – higher stress capacity

− Group center, topological center



Goal: Reduce reconfiguration time and preserve tree quality

Target metrics:

Stress – node degree in the tree Diameter – maximum pairwise latency Total tree cost – sum of edge weights Reconfiguration time – algorithm time Edge change – link changes in a reconfig.

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Research - Group Dynamics



Dynamic membership – nodes join and leave the multicast tree dynamically

− Must insert and remove nodes online



Needs algorithm to reconfigure the tree



Contradictory goals

− Reconfiguration time – fast algorithms

− Efficient tree – low diameter, low total cost.

− Tree stability – low number of edge changes between reconfigurations

− Stress - Bound node degree



Impossible to satisfy every goal

Target metrics:

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Research – Reconfiguration Set

 Reconfiguration set – nodes involved in reconfiguration

 Entire group

− Pros: Tree efficiency

− Cons: High reconfiguration time, tree stability

 Reduced size of reconfiguration set

− Pros: Low reconfiguration time, increased stability

− Cons: Reduced tree efficiency

Target metrics:

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Research – Reconfiguration Set

 Reconfiguration set – nodes involved in reconfiguration

 Entire group

− Cons: High reconfiguration time, tree stability

 Reduced size of reconfiguration set

− Pros: Low reconfiguration time, increased stability

− Cons: Reduced tree efficiency

Target metrics:

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Dynamic algorithms: Insert strategies



Basic insertion choices

− Insert as leaf – no edge change

− Insert and reconfigure – increased tree efficiency but reconfiguration time



Implemented some (8) insert algorithms

− I-MC : insert minimum cost edge

− I-CN : Insert Center Node

− I-MDDL : insert minimum diameter degree limited edge

Node is joining

Connect to tree as leaf Insert strong core Use as intersection

Three configuration examples

I-MC I-CN I-MDDL

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Dynamic algorithms: Remove strategies



Basic remove choices

− Remove leaf – easy

− Remove non-leaf – MUST reconfigure

• reconfigure and add/remove non-member node



Implemented some (11) algorithms

− RK – convert member to a non-member node

− RTR-MC – reconfiguration includes neighbor of leaving node

− RTR-P – larger reconfiguration, prune non members

RTR-MC RTR-P

m is leaving ^sⁿ

RK

reconfiguration set convert to

non member node

well connected node member node

s_n

s_n s_o

non member node

s_n

s_n s_n s_n s_n s_n

s_o

s_o s_o

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Dynamic Algorithms – Insert/Remove

MDDL I-MDDL 25

20

15

10

5 0 20 40 60 80 100

diameter

group size / number of nodes

Dynamic algorithm: I-MDDL/RTR-MC

MDDL

I-MDDL 25

20

15

10

5 0 20 40 60 80 100

diameter

group size / number of nodes

Dynamic algorithm: I-MDDL/RTR-P

RTR-P RTR-MC

leaving

reconfigure set reconfigure set

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Dynamic Algorithms - Results

 Dynamic Algorithms – insert/remove (reconfigure smaller parts of a tree)

− Execution time is low for dynamic algorithms

− Low number of edge changes between configurations.

 Main issues: tree efficiency suffers

− Always local optimizations

− Crowded with non member nodes

 Algorithms address issues: Vary reconfiguration set size, prune non-members, switch non members to stronger cores

− Cons: Increased reconfiguration time, reduced stability

Edge changes – remove algorithms (100 nodes)

Non-member nodes

SPH RTR-P RTR-MC RK

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Latency estimation in the Internet

 Difficult to maintain updated information between all

node pairs

 Evaluate use of latency estimation

− Netvigator

• landmark-based technique

• establish a fully meshed network of landmark nodes

• measure distance to each of them

− Vivaldi

• multi-dimensional scaling technique

• model nodes as masses connected by spring

• relax springs until minimal energy state is reached

• works with an arbitrary number of measurements

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diameter difference: all-to-all vs. estimation (seconds)

CDF (pairs)

Latency estimation in the Internet

Divergance for the degree-limited shortest path tree

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0.1 0.15 0.2 0.25 0.35 0.3

Diameter based on Vivaldi estimation Diameter based on Netvigator estimation Diameter based on ping measurements

Multicast Group Management for Interactive Distributed Applications