DECENTRALIZED SCALE-FREE NETWORK CONSTRUCTION AND LOAD BALANCING IN MASSIVE MULTIUSER VIRTUAL ENVIRONMENTS Markus Esch, Eric Tobias - University of Luxembourg
MOTIVATION HyperVerse project Massive Multiuser Virtual Environment (MMVE) Global-scale Open Similar to the Web D Web
TWO TIER INFRASTRUCTURE Client overlay: Data distribution PS PS PS PS PS PS PS Public Server overlay: Reliable hosting Client management
BACKBONE OVERLAY Interconnect Public Servers Requirements: Distribute world surface Distribute load Heterogeneous set of Public Serves. Consider high client dynamics
CONCEPT Voronoi diagram Server position corresponding to capacities Adapt position to dynamic load shifts Scale-free link structure
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
LOCAL RULES Centering Move towards center of mass B Minimize nodes at Voronoi borders A
LOCAL RULES Unload Neighbors Move towards overburdened servers / & '()*, -()*,, '()*+, -().,!!# '()*,, -(*, '()*+, -(),, # '()+, -(), $ " '(),, -()*, % '()1, -()*, ')2)'45678)-)2)9:55)-;;8))))))))2)6<:=567:7
LOCAL RULES Keeping the massorder Having regions with high masses hosted by powerful servers 1. $%&'() *%&')) $%&'/) *%&) $! *!! $ # * # $%&')) *%&) # $%&'() *%&+) $%&/) *%&(), " $%&()) *%&'() #! - $%&?) *%&') $&2&$45678&*&2&9:55&*;;8&&&&&&&&2&96< =9>
LOCAL RULES Swapping :; Hotspot: The spot in a voronoi cell with the biggest mass within a certain radius :; := ' (?)A; & (?)<;; <; % (?)=;! (?)A; # (?)@; <= $ (?)>=; :; " (?)A= >;
GLOBAL RULES Jumping Epidemic aggregation Active Search ()*+,*-./1,2 #" $ & ' Active Pull %!" (4.'""
LOAD BALANCING Simulation Video:
OVERLOAD MASS Overload Mass Ratio 45 4 5 25 2 15 1 5 Overall Load: 8% 1 2 4 5 6 7 Step Overload Mass Ratio 4 5 25 2 15 1 5 Overall Load: 6% 1 2 4 5 6 7 Step Overload Mass Ratio 25 2 15 1 5 Overall Load: 4% 1 2 4 5 6 7 Step Self-organization scheme Unified distribution
OVERLOAD DURATION 45 4 5 Duration in Steps 25 2 15 1 5 1 2 4 5 6 7 Step Unified scheme, 4% Unified scheme, 6% Unified scheme, 8% Self-organization scheme, 4% Self-organization scheme, 6% Self-organization scheme, 8%
HOTSPOT ACCURACY 1 9 8 Accuracy 7 6 5 4 1 2 4 5 6 7 Step 4% 6% 8%
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-
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-
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-
ALGORITHM N
ALGORITHM N
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
γ = 2.1; M = 1 1 1 γ = ; M = 1 1 γ = 2.7; M = 1 1 1 1 Nodes 1 Nodes 1 Nodes 1 1 1 1 1 1 1 1 1 1 Degree 1 1 1 1 1 1 Degree 1 1 1 1 1 1 Degree γ = 2.1; M = 2 1 1 γ = ; M = 2 1 γ = 2.7; M = 2 1 1 1 NODE DEGREE Nodes 1 Nodes 1 Nodes 1 1 1 1 DISTRIBUTION 1 1 1 1 1 1 Degree 1 1 1 1 1 1 Degree 1 1 1 1 1 1 Degree 1 γ = 2.1; M = 1 γ = ; M = 1 γ = 2.7; M = 1 1 1 Nodes 1 Nodes 1 Nodes 1 1 1 1 1 1 1 1 1 1 Degree 1 1 1 1 1 1 Degree 1 1 1 1 1 1 Degree 5 Nodes 1 Nodes
POWER LAW EXPONENT.2 M = 1.2 M = 2.2 M = Power-Law Exponent 2 Power-Law Exponent 2 Power-Law Exponent 2 1 4 7 1 1 4 7 1 1 4 7 1 γ = 2.1 γ = γ = 2.7
CLUSTERING COEFFICIENT.4 γ = 2.1; M = 1.4 γ = ; M = 1.4 γ = 2.7; M = 1 Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 1 4 7 1.4 γ = 2.1; M = 2.4 γ = ; M = 2.4 γ = 2.7; M = 2 Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 1 4 7 1.4 γ = 2.1; M =.4 γ = ; M =.4 γ = 2.7; M = Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 Scale-Free Graph Random Graph
AVERAGE SHORTEST PATH Average Shortest Path γ = 2.1; M = 1.4.2 2 Average Shortest Path γ = ; M = 1.4.2 2 Average Shortest Path γ = 2.7; M = 1.4.2 2 1 4 7 1 Average Shortest Path γ = 2.1; M = 2.4.2 2 Average Shortest Path γ = ; M = 2.4.2 2 Average Shortest Path γ = 2.7; M = 2.4.2 2 1 4 7 1 Average Shortest Path γ = 2.1; M =.4.2 2 Average Shortest Path γ = ; M =.4.2 2 Average Shortest Path γ = 2.7; M =.4.2 2 Scale-Free Graph Random Graph
NETWORK DIAMETER 4.5 γ = 2.1; M = 1 4.5 γ = ; M = 1 4.5 γ = 2.7; M = 1 4 4 4 Diameter.5 Diameter.5 Diameter.5 2.5 2.5 2.5 1 4 7 1 4.5 γ = 2.1; M = 2 4.5 γ = ; M = 2 4.5 γ = 2.7; M = 2 4 4 4 Diameter.5 Diameter.5 Diameter.5 2.5 2.5 2.5 1 4 7 1 4.5 γ = 2.1; M = 4.5 γ = ; M = 4.5 γ = 2.7; M = 4 4 4 Diameter.5 Diameter.5 Diameter.5 2.5 2.5 2.5
CHURN SIMULATIONS = 1.2 M = 1 Power Law Exponent. 2. γ = 2.1 γ = γ = 2.7 1 2 4 5 Step
CHURN SIMULATIONS.4 γ = 2.1; M = 1.4 γ = ; M = 1.4 γ = 2.7; M = 1 Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 Clustering Coefficient.5..25.2.15.1.5 1 2 4 5 1 2 4 5 1 2 4 5 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.2 2 1 2 4 5 Average Shortest Path.2 2 1 2 4 5 Average Shortest Path.2 2 1 2 4 5 Step Step Step Scale-Free Graph Random Graph 4.5 γ = 2.1; M = 1 4.5 γ = ; M = 1 4.5 γ = 2.7; M = 1 4 4 4 Diameter.5 Diameter.5 Diameter.5 2.5 2.5 2.5 1 2 4 5 1 2 4 5 1 2 4 5 Step Step Step
CONCLUSION Backbone overlay for MMVE scenario Dynamic load balancing Scale-free small-world network Simulation results show viability for intended scenario
THANK YOU FOR YOUR ATTENTION...... QUESTIONS?