Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
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Transcript of Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Socially-aware DHTs for DOSNs 1
Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Muhammad Anis Uddin Nasir, Sarunas Girdzijauskas, Nicolas KourtellisKTH Royal Institute of Technology
Telefonica Research
Socially-aware DHTs for DOSNs 2
Social Networks
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Social Networks
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Social Network Services
• Search
• Information Dissemination
• Storage
• Profile Management
• Third-Party Application
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Privacy and Scalability
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Decentralized Online Social Networks
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Research Question
“Can we design an overlay for decentralized online social networks that can support light
weight, low-cost devices such as mobile phones, web browsers"
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Decentralized Online Social Networks
• Low Management Overhead
• Scalability
• Bounded Degree
• Reliability and Security
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Distributed Hash Tables
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Distributed Hash Tables
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Social Networks on DHT
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Socially-aware Distributed Hash Tables
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Problem Formulation
• – where V is the set of vertices and E is the set of edges,
connecting the vertices.
• A– Neighbors of node i are all the nodes that share an edge
with node i
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Problem Formulation
• Social Ties
• Overlay Distance
• Optimization Problem
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Intuition
• Socially-Aware Distributed Hash Tables
• Gossip-based Algorithm– Initialization Phase– Refinement Phase
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Gossip-based Algorithm
• Initialization 0
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Neighbor Selection
• Direct: A node selects one of his friends in the social graph uniformly at random.
• Greedy: A node selects its friend with strongest tie.
• Smart: A node selects a node m uniformly at random from its top k strongest friends.
• Random: A node i selects a random other node.
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Gossip-based Algorithm
• Refinement 0
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Gossip-based Algorithm
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• Convergence
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Gossip-based Algorithm
• Reduces Communication Overhead
• Improves Security
• Increases Reliability
• Improves Performance
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Experiments
• What is the tuning cost of the algorithm?
• How does node ordering impact algorithm convergence?
• What are the performance gains with respect to lookup latency and reliability?
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Evaluation
• Comparison with Symphony overlay
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Metrics
• Algorithm Tuning– Peer Selection Schemes
• Social Networks– Lookup Latency– Reliability
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Q1: Peer Selection Scheme
• Direct, Random and Smart Neighbor selection schemes perform similar
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Q2: Execution Ordering• Convergence is fast with ordered execution of the
algorithm
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Q3: Performance
• SD has less improvement due to lack of clustering in the social graph
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Q3: Reliability
• Reliability improves significantly in terms of finger table
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Q3: Reliability
• Significant improvement in the reliability in terms of connections
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Conclusion
• Socially-aware distributed hash tables improves the performance for decentralized online social network
• We propose a gossip-based algorithm that considers social ties to achieve social-awareness in DHT
• We show that our approach reduces the lookup latency by almost 30% and improves the reliability of the communication by nearly 10% via trusted contacts.
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Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Muhammad Anis Uddin Nasir, Sarunas Girdzijauskas, Nicolas KourtellisKTH Royal Institute of Technology
Telefonica Research
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Further Read
• http://arxiv.org/pdf/1508.05591v2.pdf
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Dealing with Failures
• Social graphs evolve in a linear fashion
• Mobility or inactivity of peers, and peer or network failure cause instability
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Q1: Migration Cost