Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
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AbstractโMany decentralized online social networks (DOSNs) have been proposed due to an increase in awareness related to privacy and scalability issues in centralized social networks. Such decentralized networks transfer processing and storage functionalities from the service providers towards the end users. DOSNs require individualistic implementation for services, (i.e., search, information dissemination, storage, and publish/subscribe). However, many of these services mostly perform social queries, where OSN users are interested in accessing information of their friends. In our work, we design a socially-aware distributed hash table (DHTs) for efficient implementation of DOSNs. In particular, we propose a gossip-based algorithm to place users in a DHT, while maximizing the social awareness among them. Through a set of experiments, 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
- 1. Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks Muhammad Anis Uddin Nasir, Sarunas Girdzijauskas, Nicolas Kourtellis KTH Royal Institute of Technology Telefonica Research Socially-aware DHTs for DOSNs 1
- 2. Social Networks Socially-aware DHTs for DOSNs 2
- 3. Social Networks Socially-aware DHTs for DOSNs 3
- 4. Social Network Services โข Search โข Information Dissemination โข Storage โข Profile Management โข Third-Party Application Socially-aware DHTs for DOSNs 4
- 5. Privacy and Scalability Socially-aware DHTs for DOSNs 5
- 6. Decentralized Online Social Networks Socially-aware DHTs for DOSNs 6
- 7. Research Question โCan we design an overlay for decentralized online social networks that can support light weight, low-cost devices such as mobile phones, web browsers" Socially-aware DHTs for DOSNs 7
- 8. Decentralized Online Social Networks โข Low Management Overhead โข Scalability โข Bounded Degree โข Reliability and Security Socially-aware DHTs for DOSNs 8
- 9. Distributed Hash Tables Socially-aware DHTs for DOSNs 9
- 10. Distributed Hash Tables 0 0.25 0.5 0.75 Socially-aware DHTs for DOSNs 10
- 11. Social Networks on DHT 0 0.25 0.5 0.75 Socially-aware DHTs for DOSNs 11
- 12. Socially-aware Distributed Hash Tables 0 0.25 0.5 0.75 Socially-aware DHTs for DOSNs 12
- 13. Problem Formulation โข โ where V is the set of vertices and E is the set of edges, connecting the vertices. โข A โ Neighbors of node i are all the nodes that share an edge with node i Socially-aware DHTs for DOSNs 13
- 14. Problem Formulation โข Social Ties โข Overlay Distance โข Optimization Problem Socially-aware DHTs for DOSNs 14
- 15. Intuition โข Socially-Aware Distributed Hash Tables โข Gossip-based Algorithm โ Initialization Phase โ Refinement Phase Socially-aware DHTs for DOSNs 15
- 16. Gossip-based Algorithm โข Initialization 0 0.25 0.5 0.75 Socially-aware DHTs for DOSNs 16
- 17. Neighbor Selection โข Direct: A node selects one of his friends in the social graph uniformly at random. โข Greedy: A node selects its friend with strongest tie. โข Smart: A node selects a node m uniformly at random from its top k strongest friends. โข Random: A node i selects a random other node. Socially-aware DHTs for DOSNs 17
- 18. Gossip-based Algorithm โข Refinement 0 0.25 0.5 0.75 Socially-aware DHTs for DOSNs 18
- 19. Gossip-based Algorithm 0 0.25 0.5 0.75 Socially-aware DHTs for DOSNs โข Convergence 19
- 20. Gossip-based Algorithm โข Reduces Communication Overhead โข Improves Security โข Increases Reliability โข Improves Performance Socially-aware DHTs for DOSNs 20
- 21. Experiments โข What is the tuning cost of the algorithm? โข How does node ordering impact algorithm convergence? โข What are the performance gains with respect to lookup latency and reliability? Socially-aware DHTs for DOSNs 21
- 22. Evaluation โข Comparison with Symphony overlay Socially-aware DHTs for DOSNs 22
- 23. Metrics โข Algorithm Tuning โ Peer Selection Schemes โข Social Networks โ Lookup Latency โ Reliability Socially-aware DHTs for DOSNs 23
- 24. Q1: Peer Selection Scheme โข Direct, Random and Smart Neighbor selection schemes perform similar Socially-aware DHTs for DOSNs 3 3.5 4 4.5 5 0 100 200 300 400 500 600 700 800 900 1000 LookupLatency Number of iterations Performance Random Neighbor Direct Neighbor Greedy Neighbor Smart Neighbor 24
- 25. Q2: Execution Ordering Socially-aware DHTs for DOSNs 3 3.5 4 4.5 5 50 100 150 200 250 300 350 400 450 500 LookupLatency Number of iterations Performance Descending Order Direct Peer Sampling Ascending Order โข Convergence is fast with ordered execution of the algorithm 25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 50 100 150 200 250 300 350 400 450 500 FractionofMigrationCost Number of Iterations Fraction of Nodes Swapping Identifiers Descending Order Direct Peer Sampling Ascending Order
- 26. Q3: Performance โข SD has less improvement due to lack of clustering in the social graph 3 3.5 4 4.5 5 5.5 6 6.5 7 FB W V SD TW LookupLatency Symphony Direct Socially-aware DHTs for DOSNs 26
- 27. Q3: Reliability โข Reliability improves significantly in terms of finger table 0.01 0.1 1 10 FB W V SD TW Reliability1% Symphony Direct Socially-aware DHTs for DOSNs 27
- 28. Q3: Reliability โข Significant improvement in the reliability in terms of connections 0.1 1 10 100 FB W V SD TW Reliability2% 1-hop 2-hop 3-hop Socially-aware DHTs for DOSNs 28
- 29. Conclusion โข Socially-aware distributed hash tables improves the performance for decentralized online social network โข We propose a gossip-based algorithm that considers social ties to achieve social-awareness in DHT โข 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. Socially-aware DHTs for DOSNs 29
- 30. Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks Muhammad Anis Uddin Nasir, Sarunas Girdzijauskas, Nicolas Kourtellis KTH Royal Institute of Technology Telefonica Research Socially-aware DHTs for DOSNs 30
- 31. Further Read โข http://arxiv.org/pdf/1508.05591v2.pdf Socially-aware DHTs for DOSNs 31
- 32. Dealing with Failures โข Social graphs evolve in a linear fashion โข Mobility or inactivity of peers, and peer or network failure cause instability Socially-aware DHTs for DOSNs 32
- 33. Q1: Migration Cost Socially-aware DHTs for DOSNs 0.001 0.01 0.1 1 0 100 200 300 400 500 600 700 800 900 1000 FractionofMigrationCost Number of Iterations Migration Cost Minimizing Hop Distance Minimizing Euclidean Distance 33
Editor's Notes
- Small world network Requirement DHT Random DHT high latency and less reliability Both network has small world property, We should be able to embed We consider social ties Gossip based algorithm Peer selection schemes Experimental section
- High Clusterization Low Diameter
- The incentive for a provider is the access to large amounts of data, which can be used for business-related purposes However, these incentives have raised privacy concerns among users.
- Each device only supports limited number of connections
- DHTs are small-world graphs embedded in some identifier space, where we know how navigate efficiently. Then you discuss the properties of social networks, and that they also are small worlds. Then you say what we would like to take a subgraph of social-netowrk, such that it resembles the properties of a DHT graph and intelligently embed it into some id space. if we do so, we can achieve navigability as in classical DHT, but mostly using only social links, with all the good "consequences": fast news-feed creation, low latency, low "spam/relay" nodes etc.
- DHTs are a very promising solution for DOSNs since they provide all required functionalities with a limited peer degree in the resulting overlays. Such overlays are small-world in nature and have efficient routing properties. However, current DHTs create such overlays solely based on the peer IDs which are assigned uniformly at random and do not reflect the social graph structure of DOSNs. This significantly downgrades the performance of DHT-based DOSNs since most of the workloads directly reflect the topology of the social graph mapped on the overlay. On a DHT- based DOSN, such requests would correspond to generating expensive relay traffic for performing simple actions on each social link.
- Nodes get an identifier from an identifier space Each node is map to a ring, where each node points to two direct neighbors (predeccessor and succeessor ) Each node further creates a long range link using a probability distribution function That guarantees small world nature by sampling more short range links and few long range links
- Lets take a toy example to understand the problem We use a ring based DHT. And place users randomly in the form of the ring Each user wants to communicate with his friends, which are shown in the example with the same color Now when a user create a request
- In a socially aware DHT we want to map each user close to their friends for example this The benefits of this approach is two fold First, routing requires less number of steps Second, routing involves only friend nodes that improves the overall reliability of the system as social friends are more likely to forward your message Third, overlay becomes simple and easy to maintain
- For a DHT, we define the distance between two nodes i and j as dij . We define two different distance metrics: 1) euclidean distance between the two node ids, and 2) lookup latency (number of hops in the overlay) between the two nodes.
- we aim to improve the performance of DHT- based DOSN services by designing a socially-aware DHT
- One the node is selected, the node will try to move closer to the node. How can we do it ? One easy way of doing it is to swap identifier to any of the neighbors of node A
- In a socially aware DHT we want to map each user close to their friends for example this The benefits of this approach is two fold First, routing requires less number of steps Second, routing involves only friend nodes that improves the overall reliability of the system as social friends are more likely to forward your message Third, overlay becomes simple and easy to maintain
- We run algorithm for 1000 iterations With four different peer selection schemes On y-axis we report the lookup latency Results show that the greedy node selection has the fastest convergence time. However, it provides minimal improvements with respect to lookup latency. The other three approaches, i.e., random, direct and smart selection, achieve better improvements in the performance of the overlay. Therefore, we use direct peer selection in the further experiments due to its simple and decentralized nature.
- On the one hand, top degree nodes are more important in the network and can affect many nodes in the overlay at once, thus, affecting its speed of convergence (could be faster, or may lead to oscillations). On the other hand, bottom degree nodes are more periphery nodes and may allow slower, but steadier, convergence.
- Small world network Requirement DHT Random DHT high latency and less reliability Both network has small world property, We should be able to embed We consider social ties Gossip based algorithm Peer selection schemes Experimental section
- Gossip-based algorithm ensures handling evolving graphs data replication providing guarantees for eventual consistency socio-incentivized networks
- We compare the migration cost of two different overlay distance function