Socially-aware Query Routing in Mobile Social Networks Andreas Konstantinidis, Demetrios Zeinalipour-Yazti Department of Computer Science, University of Cyprus, Cyprus and Kun Yang School of Computer Science and Electronic Engineering, University of Essex, UK Hellenic Data Management Symposium, 2010

Speaker: Andreas Konstantinidis – University of Cyprus Social Networks (on the Web) Social Network: a set of people or groups of people with some pattern of contact or interaction among them –Attracted billions of active users under major online social network systems –Examples: MySpace, Facebook, Twitter

Speaker: Andreas Konstantinidis – University of Cyprus Mobile Social Networks (MoSoNets) Mobile Social Network: Social Network applications for smartphone devices. –Examples: Google Latitude and Google Buzz, Foursquare, Gowalla and Loopt. Smartphone: offers more advanced computing and connectivity than a basic 'feature phone'. E.g., OS: Android, Nokia’s Maemo, Apple X

Speaker: Andreas Konstantinidis – University of Cyprus Mobile Social Networks (MoSoNets) Mobile Social Network applications are projected to grow in the future. Google Latitude already reports over 3 Million Users with more than 1 Million Users available online concurrently.

Speaker: Andreas Konstantinidis – University of Cyprus Motivation Numerous research challenges arise in the context of Mobile Social Networks –Data Management Challenges: Query Processing and Retrieval, Storage (Cloud vs. Local), Access Methods, etc. –Mobility Challenges: Context Awareness, etc. –Social Challenges: Privacy, etc. –System Challenges: Architectures, Platforms etc. In this work we attempt to exploit knowledge about the underlying social network in order to improve query routing in Mobile Social Networks.

Speaker: Andreas Konstantinidis – University of Cyprus Example Scenario Scenario: Five (5) users moving in Lower Manhattan collecting data (video, photos, sound, rss, …) U1 U2 U4 U5 U3

Speaker: Andreas Konstantinidis – University of Cyprus Example Scenario: Assumptions Assumptions Users feature “long-range” connectivity (e.g., WiFi | 3G) and “short-range” connectivity (e.g., Bluetooth) Communication Links are Expensive (i.e., due to energy and bandwidth constraints) >> Bandwidth Constraints 4G nets in the US (Sprint, AT&T) promise 3- 10MBps but offer as low as 0,6MBps. >> Power Constraints : 0.40W – No connections 0.52W – Bluetooth Connection Established 1.73W – Download 120KBps via 3G

Speaker: Andreas Konstantinidis – University of Cyprus Example Scenario Find Video of street artists performing right now? U1U2U3U4 U5 {(X,Y,T,obj) | X,Y: spatial, T: temporal, Obj: object} Fact: Content is Distributed and there is no Global Index! Problem: How to find the answer without flooding the SmartNet Mobile Social Networking Service

Speaker: Andreas Konstantinidis – University of Cyprus Example Scenario U1U2U3U4 U5 Interest Matrix (Profile) ArtsFood Cinema U1 X U2 XX U3 X U4 XX … Query Routing Tree (T) Disseminate Query using T MoSoNet Service Query Processor Social Graph (G) (WiFi| 3G) Bluetooth (cheaper) Bluetooth (cheaper)

Speaker: Andreas Konstantinidis – University of Cyprus Example Scenario U1U2U3U4 U5 Download Photo\Video (via WiFi|3G|Bluetooth) MSN Service Query Processor We do not consider this phase in greater detail

Speaker: Andreas Konstantinidis – University of Cyprus Overview Introduction and Motivation Problem Formulation Multi-objective Optimization of Query Routing Trees Experimental Setup & Evaluation Current/Future work

Speaker: Andreas Konstantinidis – University of Cyprus Why Use Query Routing Trees (T)? –Avoid Flooding the Network w/ Queries (Scalable) More Efficient in terms of Energy, Communication, etc. –Better Query response quality An out-of-sync centralized data repository performs worse than a “live” decentralized data repository. –Optimally exploit short vs. long range communication links (i.e., Bluetooth vs. WiFi|3G) –Finally, it offers more Privacy (No single authority has a global view of all data). Query Routing Trees (T)

Speaker: Andreas Konstantinidis – University of Cyprus Problem: Construct a Query Routing Tree (T), for a mobile social network, that optimizes the following three (3) conflicting objectives, concurrently: –Α) Minimize Overhead, in conducting the query –B) Maximize (Query Result) Quality. –C) Maximize Social Interaction (i.e., exploit interactions in the physical space) More formal measures defined next… Query Routing Tree Problem (QRTP)

Speaker: Andreas Konstantinidis – University of Cyprus QRTP: Objective 1 A) Minimum Overhead: a lower number of answers, assures lower traffic load and lower bandwidth consumption. Smaller Tree, Less Answers  Lower Quality!   Lower Overhead  Neutral Interactions 

Speaker: Andreas Konstantinidis – University of Cyprus QRTP: Objective 2 B) Maximize Quality: higher number of relevant answers based on interests matrix. a higher response requires more traffic load Larger Tree, More Answers  Higher Quality!  Higher Overhead   Neutral Interactions 

Speaker: Andreas Konstantinidis – University of Cyprus QRTP: Objective 3 C) Maximize Social Interaction: Frequency of user interaction in physical space. –How this can be determined? Based on Bluetooth interactions of users in physical space –Solution 1 –Solution 1: few users with HIGH SI  Lower Quality!   Lower Overhead –Solution 2 –Solution 2: many users with HIGH SI  High Quality!  High Overhead 

Speaker: Andreas Konstantinidis – University of Cyprus Overview Introduction and Motivation Problem Formulation Multi-objective Optimization of Query Routing Trees Experimental Setup & Evaluation Current/Future work

Speaker: Andreas Konstantinidis – University of Cyprus Multi-Objective Optimization (MOO) Classical single objective optimization has the form: –where x is a discrete vector representing a solution (e.g. a network design, a route) – y is a real value representing the solution quality –f is the objective function Multi-Objective Optimization –No single solution is optimal under all objectives –Improve one deteriorates the others –Partial ordering of solutions (“y dominates z“) –Pareto optimal set (maps to the Pareto Front (PF) ) non-dominated solutions in PF dominated solution PF f2f2 f1f1 y z x

Speaker: Andreas Konstantinidis – University of Cyprus MOO Approaches: MOEAs EAs to MOEAs, good in obtaining a set of non-dominated solutions in a single run: –Deal with a population of solutions. –Converge towards near- optimal solutions fast. Main steps of EAs: –Objective functions –Encoding Representation –Initialization –Genetic components Selection Crossover Mutation –Update (elitism: use of archive) Initialization Selection Reproduction: Crossover Mutation Survival Update …… … …

Speaker: Andreas Konstantinidis – University of Cyprus KEY CHRACTERISTICS Decomposes a MOP into a set of SOPs using any technique for aggregating functions: –e.g. weighted sum, Tchebycheff: Tackles them simultaneously, using neighbourhood information and SOO techniques. Hybridize with local-search based techniques. Incorporate problem-specific knowledge. Andreas Konstantinidis, Kun Yang, Qingfu Zhang and Demetrios Zeinalipour-Yazti, "A Multi-Objective Evolutionary Algorithm for the Deployment and Power Assignment Problem in Wireless Sensor Networks", SI-New Network Paradigms, Computer Networks, vol. 54, pp , MOEA/D framework

Speaker: Andreas Konstantinidis – University of Cyprus QRTP Operation Summary

Speaker: Andreas Konstantinidis – University of Cyprus Overview Introduction and Motivation Problem Formulation Multi-objective Optimization of Query Routing Trees Experimental Setup & Evaluation Current/Future work

Speaker: Andreas Konstantinidis – University of Cyprus Experimental Setup –Simulator: We have implemented a trace- driven simulator in Java (a good starting point for evaluating ideas at a preliminary stage) –Datasets: Synthetic based on Random Distributions (for Social Interaction and Interest Matrix) –Query-By-Example: SELECT IP, Filename FROM MobileSocialNetwork WHERE similar(multimedia-object) –Evaluation Metrics: Next Slide

Speaker: Andreas Konstantinidis – University of Cyprus Performance Metrics Evaluation metrics –Quality & diversity of solutions (using five metrics). –Bandwidth cost BW(X): the product of n ≤ N in tree X and the number of fragmented packets f of size MTU for data of a particular type (e.g. video, image, ) and size l: –Latency L(X): the sum of the information of size f×MTU, transferred per node over a specific wireless network (e.g. WiFi) with a data rate DR: where f = l/(MTU −hd) and hd is the TCP/IP header size.

Speaker: Andreas Konstantinidis – University of Cyprus Results & Discussion MOEA/D vs NSGA-II NSGA-II the state-of-the-art in MOEAs based on Pareto dominance. Pairs of two objective are used. Similar conclusions for the third objective. Higher Quality of QRTs Higher number of Non-dominated Solutions Better Diversity

Speaker: Andreas Konstantinidis – University of Cyprus Bandwidth Consumed during Searches A) Agnostic Approach: Search by flooding. B) Informed Approach: Search over Optimal QRT. Results & Discussion 50GB 7GB 20MB 180MB Standard Deviation is low

Speaker: Andreas Konstantinidis – University of Cyprus Overview Introduction and Motivation Problem Formulation Multi-objective Optimization of Query Routing Trees Experimental Setup & Evaluation Conclusions and Future work

Speaker: Andreas Konstantinidis – University of Cyprus Conclusions and Future Work Mobile Social Networks are a new area with many new opportunities. In the future we aim to: –Deploy more realistic mobility models (GEOLife GPS Trajectories by Microsoft Asia). –Real implementation using Android technology. –Use realistic data sets for generating the interests matrix (currently working on DBLP dataset). –Evaluate the time cost for solving the QRT problem on larger-scale information spaces. Future: –Hybridization of MOEA/D with local-search heuristics.

Speaker: Andreas Konstantinidis – University of Cyprus Socially-aware Query Routing in Mobile Networks Thank you! Questions? Andreas Konstantinidis University of Cyprus