DECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS

Transcription

1 DECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS Markus Esch, Eric Tobias - University of Luxembourg

2 MOTIVATION HyperVerse project Massive Multiuser Virtual Environment (MMVE) Global-scale Open Similar to the Web D Web

3 TWO TIER INFRASTRUCTURE Client overlay: Data distribution PS PS PS PS PS PS PS Public Server overlay: Reliable hosting Client management

4 BACKBONE OVERLAY Interconnect Public Servers Requirements: Distribute world surface Distribute load Heterogeneous set of Public Serves. Consider high client dynamics

5 CONCEPT Voronoi diagram Server position corresponding to capacities Adapt position to dynamic load shifts Scale-free link structure

6 LOAD BALANCING Assign object masses and Public Server payload Set of rules applied by Public Servers Local Rules Handle local load imbalances Global Rules Handle global load imbalances

7 LOCAL RULES Centering Move towards center of mass B Minimize nodes at Voronoi borders A

8 LOCAL RULES Unload Neighbors Move towards overburdened servers / & '()*, -()*,, '()*+, -().,!!# '()*,, -(*, '()*+, -(),, # '()+, -(), $ " '(),, -()*, % '()1, -()*, ')2)'45678)-)2)9:55)-;;8))))))))2)6<:=567:7

9 LOCAL RULES Keeping the massorder Having regions with high masses hosted by powerful servers 1. $%&'() *%&')) $%&'/) *%&) $! *!! $ # * # $%&')) *%&) # $%&'() *%&+) $%&/) *%&(), " $%&()) *%&'() #! - $%&?) *%&') $&2&$45678&*&2&9:55&*;;8&&&&&&&&2&96< =9>

10 LOCAL RULES Swapping :; Hotspot: The spot in a voronoi cell with the biggest mass within a certain radius :; := ' (?)A; & (?)<;; <; % (?)=;! (?)A; # (?)@; <= $ (?)>=; :; " (?)A= >;

11 GLOBAL RULES Jumping Epidemic aggregation Active Search ()*+,*-./1,2 #" $ & ' Active Pull %!" (4.'""

12 LOAD BALANCING Simulation Video:

13 OVERLOAD MASS Overload Mass Ratio Overall Load: 8% Step Overload Mass Ratio Overall Load: 6% Step Overload Mass Ratio Overall Load: 4% Step Self-organization scheme Unified distribution

14 OVERLOAD DURATION Duration in Steps Step Unified scheme, 4% Unified scheme, 6% Unified scheme, 8% Self-organization scheme, 4% Self-organization scheme, 6% Self-organization scheme, 8%

15 HOTSPOT ACCURACY Accuracy Step 4% 6% 8%

16 SCALE-FREE LINK STRUCTURE Immediate Neighbors & Fare Neighbors Scale-free Node Degree Distribution: P(k) k -γ (γ (2,)) Expect scale-free capacity distribution Observed in WWW Π(p i )= p i p max probability distribution a joining node estab-

17 SCALE-FREE LINK STRUCTURE Immediate Neighbors & Fare Neighbors Scale-free Node Degree Distribution: P(k) k -γ (γ (2,)) Expect scale-free capacity distribution Observed in WWW Π(p i )= p i p max probability distribution a joining node estab-

18 SCALE-FREE LINK STRUCTURE Immediate Neighbors & Fare Neighbors Scale-free Node Degree Distribution: P(k) k -γ (γ (2,)) Assume scale-free capacity distribution Observed in WWW Π(p i )= p i p max probability distribution a joining node estab-

19 ALGORITHM N

20 ALGORITHM N

21 N Web sites increases faster due to their high pro time, the providers take care for allocating capacities. That means, the scale-free link s existence of hubs with sufficient capacities em organizing manner from the different popular Transferring this observation to our scenario o environment, it means, that Public Servers ho lar world objects automatically exhibit higher this is automatically ensured by the object the scale-free capacity distribution thus emerg we just have to construct the link structure this reason, we establish links to Public Se capacities, with a higher probability. Hence connected to an existing node i with probabil on the payload p i of i and the maximum network p max : ALGORITHM Π(p i )= p i p max Based on this probability distribution a joi lishes M (M >; M is a fixed parameter o

22 γ = 2.1; M = γ = ; M = 1 1 γ = 2.7; M = Nodes 1 Nodes 1 Nodes Degree Degree Degree γ = 2.1; M = γ = ; M = 2 1 γ = 2.7; M = NODE DEGREE Nodes 1 Nodes 1 Nodes DISTRIBUTION Degree Degree Degree 1 γ = 2.1; M = 1 γ = ; M = 1 γ = 2.7; M = Nodes 1 Nodes 1 Nodes Degree Degree Degree 5 Nodes 1 Nodes

23 POWER LAW EXPONENT.2 M = 1.2 M = 2.2 M = Power-Law Exponent 2 Power-Law Exponent 2 Power-Law Exponent γ = 2.1 γ = γ = 2.7

24 CLUSTERING COEFFICIENT.4 γ = 2.1; M = 1.4 γ = ; M = 1.4 γ = 2.7; M = 1 Clustering Coefficient Clustering Coefficient Clustering Coefficient γ = 2.1; M = 2.4 γ = ; M = 2.4 γ = 2.7; M = 2 Clustering Coefficient Clustering Coefficient Clustering Coefficient γ = 2.1; M =.4 γ = ; M =.4 γ = 2.7; M = Clustering Coefficient Clustering Coefficient Clustering Coefficient Scale-Free Graph Random Graph

25 AVERAGE SHORTEST PATH Average Shortest Path γ = 2.1; M = Average Shortest Path γ = ; M = Average Shortest Path γ = 2.7; M = Average Shortest Path γ = 2.1; M = Average Shortest Path γ = ; M = Average Shortest Path γ = 2.7; M = Average Shortest Path γ = 2.1; M = Average Shortest Path γ = ; M = Average Shortest Path γ = 2.7; M = Scale-Free Graph Random Graph

26 NETWORK DIAMETER 4.5 γ = 2.1; M = γ = ; M = γ = 2.7; M = Diameter.5 Diameter.5 Diameter γ = 2.1; M = γ = ; M = γ = 2.7; M = Diameter.5 Diameter.5 Diameter γ = 2.1; M = 4.5 γ = ; M = 4.5 γ = 2.7; M = Diameter.5 Diameter.5 Diameter

27 CHURN SIMULATIONS = 1.2 M = 1 Power Law Exponent. 2. γ = 2.1 γ = γ = Step

28 CHURN SIMULATIONS.4 γ = 2.1; M = 1.4 γ = ; M = 1.4 γ = 2.7; M = 1 Clustering Coefficient Clustering Coefficient Clustering Coefficient Step Step Step Scale-Free Graph Random Graph.4 γ = 2.1; M = 1.4 γ = ; M = 1.4 γ = 2.7; M = 1 Average Shortest Path Average Shortest Path Average Shortest Path Step Step Step Scale-Free Graph Random Graph 4.5 γ = 2.1; M = γ = ; M = γ = 2.7; M = Diameter.5 Diameter.5 Diameter Step Step Step

29 CONCLUSION Backbone overlay for MMVE scenario Dynamic load balancing Scale-free small-world network Simulation results show viability for intended scenario

30 THANK YOU FOR YOUR ATTENTION QUESTIONS?