scaling online social networks: extended spar using gossip learning
TRANSCRIPT
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling Online Social Networks: extended SPARusing Gossip Learning
Presented by: Muhammad Anis uddin NasirCoworker: Maria Stylianou
Supervised by: Sarunas Girdzijauskas
KTH Royal Institute of Technology
December 5, 2012
Muhammad Anis uddin Nasir Scaling Online Social Networks 1/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
1 Motivation
2 AlgorithmsSPARJA-BE-JA
3 Our ContributionChallengesProposed Algorithm
4 EvaluationDatasetsImplementationResults
5 Conclusion
Muhammad Anis uddin Nasir Scaling Online Social Networks 2/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Online Social Networks
Muhammad Anis uddin Nasir Scaling Online Social Networks 3/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scalability
Hardware Scalability
Application Scalability
Muhammad Anis uddin Nasir Scaling Online Social Networks 4/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scalability
Hardware Scalability
Application Scalability
Muhammad Anis uddin Nasir Scaling Online Social Networks 4/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical Scaling
Full ReplicationData LocalityHigh Cost
Horizontal Scaling
ShardingDisjoint DataPartitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical ScalingFull Replication
Data LocalityHigh Cost
Horizontal Scaling
ShardingDisjoint DataPartitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical ScalingFull ReplicationData Locality
High Cost
Horizontal Scaling
ShardingDisjoint DataPartitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical ScalingFull ReplicationData LocalityHigh Cost
Horizontal Scaling
ShardingDisjoint DataPartitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical ScalingFull ReplicationData LocalityHigh Cost
Horizontal Scaling
ShardingDisjoint DataPartitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical ScalingFull ReplicationData LocalityHigh Cost
Horizontal ScalingSharding
Disjoint DataPartitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical ScalingFull ReplicationData LocalityHigh Cost
Horizontal ScalingShardingDisjoint Data
Partitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling
Vertical ScalingFull ReplicationData LocalityHigh Cost
Horizontal ScalingShardingDisjoint DataPartitioning OSNs
Muhammad Anis uddin Nasir Scaling Online Social Networks 5/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
1 Motivation
2 AlgorithmsSPARJA-BE-JA
3 Our ContributionChallengesProposed Algorithm
4 EvaluationDatasetsImplementationResults
5 Conclusion
Muhammad Anis uddin Nasir Scaling Online Social Networks 6/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Local Semantics
Load Balancing
Fault Tolerant
Dynamic
Low Replication Overhead
Muhammad Anis uddin Nasir Scaling Online Social Networks 7/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Local Semantics
Load Balancing
Fault Tolerant
Dynamic
Low Replication Overhead
Muhammad Anis uddin Nasir Scaling Online Social Networks 7/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Local Semantics
Load Balancing
Fault Tolerant
Dynamic
Low Replication Overhead
Muhammad Anis uddin Nasir Scaling Online Social Networks 7/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Local Semantics
Load Balancing
Fault Tolerant
Dynamic
Low Replication Overhead
Muhammad Anis uddin Nasir Scaling Online Social Networks 7/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Local Semantics
Load Balancing
Fault Tolerant
Dynamic
Low Replication Overhead
Muhammad Anis uddin Nasir Scaling Online Social Networks 7/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Architecture
Partition Manager
Directory Service
Local DirectoryService
ReplicationManager
Muhammad Anis uddin Nasir Scaling Online Social Networks 8/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Architecture
Partition Manager
Directory Service
Local DirectoryService
ReplicationManager
Muhammad Anis uddin Nasir Scaling Online Social Networks 8/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Architecture
Partition Manager
Directory Service
Local DirectoryService
ReplicationManager
Muhammad Anis uddin Nasir Scaling Online Social Networks 8/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Architecture
Partition Manager
Directory Service
Local DirectoryService
ReplicationManager
Muhammad Anis uddin Nasir Scaling Online Social Networks 8/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
Heuristic
Greedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove
3 Configurations
Server Add/Remove
RedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy Optimization
Local Search
Node Add/Remove
Edge Add/Remove
3 Configurations
Server Add/Remove
RedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove
3 Configurations
Server Add/Remove
RedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove
3 Configurations
Server Add/Remove
RedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove
3 Configurations
Server Add/Remove
RedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/Remove
RedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/Remove
RedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/RemoveRedistribution
Let it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/RemoveRedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/RemoveRedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/RemoveRedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/RemoveRedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
SPAR Algorithm
HeuristicGreedy OptimizationLocal Search
Node Add/Remove
Edge Add/Remove3 Configurations
Server Add/RemoveRedistributionLet it fill
Muhammad Anis uddin Nasir Scaling Online Social Networks 9/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Distributed Partitioning
k-way Partitioning
Load Balancing
Low Inter-communicationOverhead
Local Search
Muhammad Anis uddin Nasir Scaling Online Social Networks 10/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Distributed Partitioning
k-way Partitioning
Load Balancing
Low Inter-communicationOverhead
Local Search
Muhammad Anis uddin Nasir Scaling Online Social Networks 10/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Distributed Partitioning
k-way Partitioning
Load Balancing
Low Inter-communicationOverhead
Local Search
Muhammad Anis uddin Nasir Scaling Online Social Networks 10/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Distributed Partitioning
k-way Partitioning
Load Balancing
Low Inter-communicationOverhead
Local Search
Muhammad Anis uddin Nasir Scaling Online Social Networks 10/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Features
Distributed Partitioning
k-way Partitioning
Load Balancing
Low Inter-communicationOverhead
Local Search
Muhammad Anis uddin Nasir Scaling Online Social Networks 10/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling Policies
LocalRandomHybrid
Swapping Policies
Energy FunctionSimulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocal
RandomHybrid
Swapping Policies
Energy FunctionSimulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandom
Hybrid
Swapping Policies
Energy FunctionSimulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandomHybrid
Swapping Policies
Energy FunctionSimulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandomHybrid
Swapping Policies
Energy FunctionSimulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandomHybrid
Swapping PoliciesEnergy Function
Simulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandomHybrid
Swapping PoliciesEnergy FunctionSimulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandomHybrid
Swapping PoliciesEnergy FunctionSimulated Annealing
Algorithm
Hybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandomHybrid
Swapping PoliciesEnergy FunctionSimulated Annealing
AlgorithmHybrid Sampling
Simulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
SPARJA-BE-JA
Overview
Sampling PoliciesLocalRandomHybrid
Swapping PoliciesEnergy FunctionSimulated Annealing
AlgorithmHybrid SamplingSimulated Annealing
Muhammad Anis uddin Nasir Scaling Online Social Networks 11/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
1 Motivation
2 AlgorithmsSPARJA-BE-JA
3 Our ContributionChallengesProposed Algorithm
4 EvaluationDatasetsImplementationResults
5 Conclusion
Muhammad Anis uddin Nasir Scaling Online Social Networks 12/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Challenges
Global View
Partition Manager
Replication Overhead
Load Balancing
Muhammad Anis uddin Nasir Scaling Online Social Networks 13/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Challenges
Global View
Partition Manager
Replication Overhead
Load Balancing
Muhammad Anis uddin Nasir Scaling Online Social Networks 13/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Challenges
Global View
Partition Manager
Replication Overhead
Load Balancing
Muhammad Anis uddin Nasir Scaling Online Social Networks 13/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Challenges
Global View
Partition Manager
Replication Overhead
Load Balancing
Muhammad Anis uddin Nasir Scaling Online Social Networks 13/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Proposed Algorithm
SPAR + JA-BE-JA
Gossip Learning
SimulatedAnnealing
Optimal Replication
Muhammad Anis uddin Nasir Scaling Online Social Networks 14/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Proposed Algorithm
SPAR + JA-BE-JA
Gossip Learning
SimulatedAnnealing
Optimal Replication
Muhammad Anis uddin Nasir Scaling Online Social Networks 14/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Proposed Algorithm
SPAR + JA-BE-JA
Gossip Learning
SimulatedAnnealing
Optimal Replication
Muhammad Anis uddin Nasir Scaling Online Social Networks 14/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
ChallengesProposed Algorithm
Proposed Algorithm
SPAR + JA-BE-JA
Gossip Learning
SimulatedAnnealing
Optimal Replication
Muhammad Anis uddin Nasir Scaling Online Social Networks 14/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
1 Motivation
2 AlgorithmsSPARJA-BE-JA
3 Our ContributionChallengesProposed Algorithm
4 EvaluationDatasetsImplementationResults
5 Conclusion
Muhammad Anis uddin Nasir Scaling Online Social Networks 15/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic Graphs
Facebook Graphs
0http://snap.stanford.edu/data/Muhammad Anis uddin Nasir Scaling Online Social Networks 16/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic Graphs
Facebook Graphs
0http://snap.stanford.edu/data/Muhammad Anis uddin Nasir Scaling Online Social Networks 16/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic GraphsRandomized
ClusteredHighly Clustered
Facebook Graphs
150 nodes, 3386 edges224 nodes, 6384 edges786 nodes, 60050 edges
0https://gephi.org/
Muhammad Anis uddin Nasir Scaling Online Social Networks 17/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic GraphsRandomizedClustered
Highly Clustered
Facebook Graphs
150 nodes, 3386 edges224 nodes, 6384 edges786 nodes, 60050 edges
0https://gephi.org/
Muhammad Anis uddin Nasir Scaling Online Social Networks 17/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic GraphsRandomizedClusteredHighly Clustered
Facebook Graphs
150 nodes, 3386 edges224 nodes, 6384 edges786 nodes, 60050 edges
0https://gephi.org/
Muhammad Anis uddin Nasir Scaling Online Social Networks 17/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic GraphsRandomizedClusteredHighly Clustered
Facebook Graphs150 nodes, 3386 edges
224 nodes, 6384 edges786 nodes, 60050 edges
0https://gephi.org/
Muhammad Anis uddin Nasir Scaling Online Social Networks 17/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic GraphsRandomizedClusteredHighly Clustered
Facebook Graphs150 nodes, 3386 edges224 nodes, 6384 edges
786 nodes, 60050 edges
0https://gephi.org/
Muhammad Anis uddin Nasir Scaling Online Social Networks 17/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Datasets
Synthetic GraphsRandomizedClusteredHighly Clustered
Facebook Graphs150 nodes, 3386 edges224 nodes, 6384 edges786 nodes, 60050 edges
0https://gephi.org/
Muhammad Anis uddin Nasir Scaling Online Social Networks 17/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Implementation
SPAR
Proposed Algorithm
MetricReplication Overhead
Muhammad Anis uddin Nasir Scaling Online Social Networks 18/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Evaluation of Replication Overhead
Replication FactorFault tolerance replicas reduce replication overheadProposed Algorithm performs better than SPAR
0replication overhead = number of replicas/number of users
Muhammad Anis uddin Nasir Scaling Online Social Networks 19/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Evaluation of Replication Overhead
Number of ServersLess Replication overhead in the case of proposed algorithmProposed Algorithm performs better in the case of highclusterization
0replication overhead = number of replicas/number of users
Muhammad Anis uddin Nasir Scaling Online Social Networks 20/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
DatasetsImplementationResults
Evaluation of Replication Overhead
Number of ServersLess Replication overhead in the case of proposed algorithmProposed Algorithm performs better in case of highclusterization
0replication overhead = number of replicas/number of users
Muhammad Anis uddin Nasir Scaling Online Social Networks 21/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
1 Motivation
2 AlgorithmsSPARJA-BE-JA
3 Our ContributionChallengesProposed Algorithm
4 EvaluationDatasetsImplementationResults
5 Conclusion
Muhammad Anis uddin Nasir Scaling Online Social Networks 22/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Conclusion
Distributed social-based partitioning
Local Semantics
Reduced Replication overhead compared to SPAR
Better load balancing using k-way partitioning
Transparent Scaling
Muhammad Anis uddin Nasir Scaling Online Social Networks 23/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Conclusion
Distributed social-based partitioning
Local Semantics
Reduced Replication overhead compared to SPAR
Better load balancing using k-way partitioning
Transparent Scaling
Muhammad Anis uddin Nasir Scaling Online Social Networks 23/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Conclusion
Distributed social-based partitioning
Local Semantics
Reduced Replication overhead compared to SPAR
Better load balancing using k-way partitioning
Transparent Scaling
Muhammad Anis uddin Nasir Scaling Online Social Networks 23/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Conclusion
Distributed social-based partitioning
Local Semantics
Reduced Replication overhead compared to SPAR
Better load balancing using k-way partitioning
Transparent Scaling
Muhammad Anis uddin Nasir Scaling Online Social Networks 23/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Conclusion
Distributed social-based partitioning
Local Semantics
Reduced Replication overhead compared to SPAR
Better load balancing using k-way partitioning
Transparent Scaling
Muhammad Anis uddin Nasir Scaling Online Social Networks 23/24
MotivationAlgorithms
Our ContributionEvaluationConclusion
Scaling Online Social Networks: extended SPARusing Gossip Learning
Presented by: Muhammad Anis uddin NasirCoworker: Maria Stylianou
Supervised by: Sarunas Girdzijauskas
KTH Royal Institute of Technology
December 5, 2012
Muhammad Anis uddin Nasir Scaling Online Social Networks 24/24