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Socially-aware Query Routing in Mobile Social Networks Hellenic Data Management Symposium, 2010 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 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, …) U5 U2 U3 U4 U1 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 310MBps 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 Mobile Social Networking Service Find Video of street artists performing right now? U1 U2 U3 Fact: Content is Distributed and there is no Global Index! Problem: How to find the answer without flooding the SmartNet U4 U5 {(X,Y,T,obj) | X,Y: spatial, T: temporal, Obj: object} Speaker: Andreas Konstantinidis – University of Cyprus Example Scenario Social Graph (G) Interest Matrix (Profile) MoSoNet Service Query Processor Query Routing Tree (T) U1 U2 Arts Food X X X U3 X U4 … Cinema X X Disseminate Query using T (WiFi| 3G) Bluetooth (cheaper) Bluetooth (cheaper) U1 U2 U3 U4 U5 Speaker: Andreas Konstantinidis – University of Cyprus Example Scenario MSN Service Query Processor We do not consider this phase in greater detail Download Photo\Video (via WiFi|3G|Bluetooth) U1 U2 U3 U4 U5 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 Query Routing Trees (T) • 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). Speaker: Andreas Konstantinidis – University of Cyprus Query Routing Tree Problem (QRTP) 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… 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. min Ov ( X ) | X | 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. n max QI ( X | Q j ) (vij | i X , n N , j Ints) i 1 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 max SI ( X ) (miz | i, ( z ) X ) i – Solution 1: few users with HIGH SI Lower Quality! Lower Overhead – 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: max y f ( x) – 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 f2 x max Z f ( x ) ( f1 ( x ), f 2 ( x),..., f m ( x )) where x ( x1 , x2 ,..., xn ) – No single solution is optimal under all objectives – Improve one deteriorates the others – Partial ordering of solutions (“y dominates z“) y, z Z , y z i 1..m, yi zi j 1..m, yi z j y z PF f1 non-dominated solutions in PF dominated solution – Pareto optimal set (maps to the Pareto Front (PF) ) * x* X | x X , f (x) f (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 nearoptimal solutions fast. Initialization Selection Main steps of EAs: – – – – … Objective functions Encoding Representation Initialization Genetic components … Update Reproduction: • Selection • Crossover • Mutation … Crossover Mutation … Survival – Update (elitism: use of archive) Speaker: Andreas Konstantinidis – University of Cyprus MOEA/D framework 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. 960-976, 2010. 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 tracedriven 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, email) 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 • • • Higher Quality of QRTs Higher number of Non-dominated Solutions Better Diversity 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. Speaker: Andreas Konstantinidis – University of Cyprus Results & Discussion • Bandwidth Consumed during Searches A) Agnostic Approach: Search by flooding. B) Informed Approach: Search over Optimal QRT. 180MB 50GB Standard Deviation is low 20MB 7GB 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. Speaker: Andreas Konstantinidis – University of Cyprus Socially-aware Query Routing in Mobile Networks Thank you! Questions? Andreas Konstantinidis University of Cyprus [email protected] Speaker: Andreas Konstantinidis – University of Cyprus