Empowering users with recommendations and providers with data analytics

Transcription

1 UNIVERSITY OF CRETE Empowering users with recommendations and providers with data analytics Maria Papadopouli Associate Professor University of Crete Foundation for Research & Technology Hellas (FORTH) This research has been funded by a GSRT Research Excellence grant ( ) & a Google Faculty Award. 1

2 Roadmap Brief introduction of our research activities in the Mobile Computing Group at FORTH-ICS u-map: empowering users a crowd-sourcing user-centric QoE recommendation system MLQoE: Modeling the perceived quality of experience (QoE) services using machine learning & data mining CoRLAB: efficient game-theoretical modeling and analysis of largescale wireless access markets 2

3 Related representative publications 1. On user-centric modular QoE prediction for VoIP based on machine-learning algorithms. IEEE Transactions on Mobile Computing (to appear) 2. On Multi-layer Modeling and Analysis of Wireless Access Markets. IEEE Transactions on Mobile Computing, vol. 14, no. 1, January Evaluation of a User-centric QoE-based Recommendation Tool for Wireless Access. ACM SIGCOMM Worksh. on Crowdsourcing & Crowdsharing of Big Data A Multi-Layer Game-Theoretical Analysis of Wireless Markets using Network Aggregations. Submitted to IEEE Transactions on Mobile Computing. 5. QoWater-A crowd-sourcing approach for assessing the water quality. CySWater 15 Cyber Physical Systems (CPS) Week, Seattle, USA 6. On User-Centric Tools for QoE-Based Recommendation and Real-Time Analysis of Large-Scale Markets. IEEE Communications Magazine Pricing for Mobile Virtual Network Operators: The contribution of u-map. IEEE DySPAN To Subscribe, or not to Subscribe: Modeling and Analysis of Service Paradigms in Cellular Markets. IEEE DySPAN Supporting Wireless Access Markets with a User-Centric QoE-Based Geo-Database. ACM MobiArch 12 36/36

4 Research Activities & Expertise Mobile peer-to-peer computing Started at Columbia University (Ph.D. thesis, since 96) [MobiHoc 01] Positioning systems Started at UNC [Percom 04, Elsevier Ad Hoc Networks 12] Wireless network measurements analysis & modeling Started at UNC [IEEE Infocom 04] Funded by UNC grants, IBM Faculty Awards, EU Hydrobionet (STREP) Quality-of-Experience (QoE) of services over wireless networks Started at UoC /FORTH [IEEE LANMAN 10] Funded by EU-Marie Curie IRG grant, GSRT grant between FORTH-UNC On user-centric modular QoE prediction for VoIP based on machine-learning alg. [IEEE Trans. on Mobile Computing] Assessment of large-scale wireless access markets Started at UoC/FORTH [IEEE DySPAN 11] On multi-layer modeling of wireless access markerts [IEEE Trans. on Mobile Computing 15] u-map: a QoE-based geo-db system for wireless users [MobiArch 12, IEEE Communications Magazine] Funded by GSRT with a Research Excellence Investigator-driven grant, Google Faculty Award 4

5 Roadmap Brief introduction of our research activities in the Mobile Computing Group at FORTH-ICS u-map: empowering users a crowd-sourcing user-centric QoE recommendation system CoRLAB: efficient game-theoretical modeling and analysis of largescale wireless access markets MLQoE: Modeling the perceived quality of experience (QoE) services using machine learning & data mining 5

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7 Technical Smarter systems Improved network performance Rapidly roll-out new services Reduce maintenance Improved user satisfaction Real-time QoE inference Early-warning Real-time statistics & trends Business Assess agreements Pricing Advertising & marketing Booking Optimize value-generating mobility & wireless services Monetize mobility & wireless service sessions Vision Smarter Commerce Analytics & Big Data Smarter Computing Convenience Time Customer Satisfaction Money 7

8 Customers Providers, regulators receive alerts, reports Wireless Base Station u-map server Data center u-map client Wireless sensor Analysis & modeling platform Organizations Resident customers 1. Monitor infrastructure performance & upload measurements on server 2. Customers upload their feedback using u-map clients on smartphones 3. Analysis & correlation of measurements and feedback 4. Recommendations, alerts, and reports are sent to all stakeholders

9 u-map: user-centric QoE geo-database for recommendations & feedback Client-to-Server architecture u-map clients on smart-phones Collect network measurements, opinion scores, customer feedback and store them locally Upload traces to u-map server Query u-map server u-map server Collects traces & stores them in spatio-temporal geo-db Responds to queries sent by users/customers & service/content providers Can be designed according to different business models: e.g., provider-driven or a third-party Strong access control that applies privacy rules Provision of incentives improved QoE in services reputation, altruism, payment (e.g., free SMS, calls) 9

10 U-map runs a localization system, which positions the user at a Google map. There are pre-determined regions for the queries (e.g., entire city, island) or the user can indicate the region of interest using the GUI/ touch screen and drawing a polygong or a path. 10

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12 u-map feeds the analysis platform with real customer data, so the business-driven assessment becomes more accurate, relevant, faster. CoRLAB u-map client Network Data center u-map server 12 Users

13 How does the network performance affect the customer experience?

14 User Satisfaction Weber-Fechner Law IQX hypothesis QoS metrics Achievable data rate Throughput, delay, packet loss Number of resource units TDMA: time slots OFDMA: frequency time QoE metrics with techno-socio-economic-psychological terms Preference on QoE or price Price, willingness to pay Perceived QoE (e.g., opinion score) Intrinsic indicators towards a service provider e.g., its brand name, perceived value/reliability content (size, searching mechanisms) To define user experience is a very hard problem & to monetize it even harder! 14

15 Our approach for predicting the QoE: Develop user-centric, service-oriented models based on network metrics Apply machine learning and data mining algorithms, such as: Decision Trees, Support Vector Regression, Artificial Neural Networks, Gaussian Naïve Bayes Find the set of predictors that minimizes the mean absolute error of a model (feature selection) Train the models based on empirical measurements collected from field studies We have demonstrated this methodology for VoIP, audio & video streaming 15

16 User-centric Main aspects of MLQoE Training the models based on collected network measurements & user feedback Automatic selection of the best algorithm & parameter tuning for predicting QoE Robustness on the number of dominant factors for predicting QoE Conservative bound on its future performance (using nested crossvalidation) Performs dimensionality reduction using feature selection algorithms Addresses the over-fitting

17 u-map is a cost-saving system. To ensure scalability, u-map is envisioned as a cloud service, taking advantage of inexpensive & flexible platforms u-map reduces overhead, administrative support & results in faster responses, better resource management, with lower cost No extra cost for monitoring Cost: u-map software includes Personalized recommendations QoE prediction DB maintenance & access control 17

18 u-map status A u-map prototype & u-map database is up & running Integrated localization Security & access control modules (on-going) Preliminary performance analysis Extensive measurements of energy consumption, delay, scalability Field subjective study with questionnaires & measurements with 22 u- map users for 2 weeks Systematic methodology according to guidelines for the evaluation of GUI e.g., friendliness, presentation, consistency u-map functionality e.g., effectiveness & delay in the discovery of new providers, and improving QoE of VoIP Applied also in other areas: water distribution networks & health 18

19 QoWater-A crowd-sourcing approach for assessing the water quality. CySWater 15 CPS Week Regulators or providers QoWater client QoWater server Customers with QoWater client Health Organization sensors Water Distribution Network sensors sensors measuring chemical parameters

20 Growth opportunities for networking, services & specialized hardware providers Trends that cause this growth: mobile device capacity growth advances in networked home cloud services user-generated content mobile services virtualization spectral efficiency (e.g., intelligent antennas, MIMOs, CDR) Larger, more complex & heterogeneous markets are formed 20

21 Traditional cellular markets Long-term licenses with exclusive access rights Client is associated with certain provider Long-term customer subscription Existing & emerging wireless access markets Larger sizes in number of clients & providers, data rates, demand More heterogeneous & diverse in populations, services, preferences, deployments, environments More complex (e.g. spectrum allocation, infrastructure, pricing, QoE) Coalitions among providers (e.g., MVNOs) Empowering customers & providers Multiple scales 21

22 Objective Business-driven assessment of Services Coalitions Infrastructure extension Roaming agreements New data plans considering various customer profiles & populations, network deployments & topology, provider profiles resource allocations Observations Evolution over long-time periods Complex to be studied analytically Critical to develop realistic simulation platforms Accuracy & scalability tradeoffs Common oversimplifications Homogeneous populations Single service/product Known simple utility functions Limited degree of realism 22

23 User models about Traffic, roaming & access Utility function Willingness to pay QoE vs. Price MVNOs Infrastructure sharing Roaming agreements VoIP, Video Prepaid card, subscription Dataplans Data, SMS, Voice, Applications QoS vs. QoE 23

24 25/36 State-of-the-art modeling approaches Microscopic modeling Large number of events Computationally expensive Not amenable to analysis Mostly limited number of entities Macroscopic modeling Aggregation and averaging Less accurate Scalable Amenable to analysis Gao et al. IEEJSAC 14, Zhuo et al., IEEE TOM 14 Duan et al., IEEE TOM 12, Gao et al., IEEE JSAC 11 Duan et al., IEEE TOM 11, Yang et al, IEEE INFOCOM 11 Yu et al., IEEE Trans. on Vehicular Technology 2010 Xing et al., IEEE JSAC 09 Al Daoud at al., IEEE Conf. on Decision Control 08 Fidan et al. IEEE Globecom 13 Niyato et al., IEEE Trans. On Computers 10 Wu et al., IEEE Trans. on Comm Zhou et al., IEEE INFOCOM 09 Niyato et al., IEEE JSAC 08 Jia et al., MobiHoc 2008

25 26/36 Modular multi-layer game-theoretical modeling framework Multiple levels of detail from the microscopic to the macroscopic Control tradeoff between accuracy & computational complexity Network decomposition & aggregation methods to reduce computational complexity Different models for the networks of providers, user utility function, traffic demand & mobility patterns Develop methodologies & algorithms that analytically estimate the Nash equilibriums (NEs) of users and providers

26 Aggregation using clustering (1 st approach) 8/36

27 34/36 Observed trends Significant computational gains through the application of the theorem of Norton QoS (e.g., variance of data rate) improves performance of spectrum markets (e.g., reduce the intensity of competition, allow for higher prices) Important role of the user utility function Degree of realism Challenges in the analytical estimation of NE (e.g., when it is not continuously differentiable)

28 35/36 Assessment of markets & services (work in progress) Multiple MNOs and MVNOs Data offloading from 3G/LTE networks to Wi-Fi infrastructure Coalitions of small MNOs Water distribution networks Health services: partnerships, mobile applications, QoE, epidemics Considering different user populations (in terms of willingness-to-pay, QoE preference, mobility), spatial distributions & pricing mechanisms

29 Related representative publications 1. On user-centric modular QoE prediction for VoIP based on machine-learning algorithms. IEEE Transactions on Mobile Computing (to appear) 2. On Multi-layer Modeling and Analysis of Wireless Access Markets. IEEE Transactions on Mobile Computing, vol. 14, no. 1, January Evaluation of a User-centric QoE-based Recommendation Tool for Wireless Access. ACM SIGCOMM Worksh. on Crowdsourcing & Crowdsharing of Big Data A Multi-Layer Game-Theoretical Analysis of Wireless Markets using Network Aggregations. Submitted to IEEE Transactions on Mobile Computing. 5. QoWater-A crowd-sourcing approach for assessing the water quality. CySWater 15 Cyber Physical Systems (CPS) Week, Seattle, USA 6. On User-Centric Tools for QoE-Based Recommendation and Real-Time Analysis of Large-Scale Markets. IEEE Communications Magazine Pricing for Mobile Virtual Network Operators: The contribution of u-map. IEEE DySPAN To Subscribe, or not to Subscribe: Modeling and Analysis of Service Paradigms in Cellular Markets. IEEE DySPAN Supporting Wireless Access Markets with a User-Centric QoE-Based Geo-Database. ACM MobiArch 12 36/36