Workload characterization of a location-based social network

  • Theo Lins
  • Adriano C. M. Pereira
  • Fabrício Benevenuto
Original Article

Abstract

Recently, there has been a large popularization of location-based social networks, such as Foursquare and Apontador, in which users can share their current locations, upload tips and make comments about places. Part of this popularity is due to facility access to the Internet through mobile devices with GPS. Despite the various efforts towards understanding characteristics of these systems, little is known about the access pattern of users in these systems. Providers of this kind of services need to deal with different challenges that could benefit of such understanding, such as content storage, performance and scalability of servers, personalization and service differentiation for users. This article aims at characterizing and modeling the patterns of requests that reach a server of a location-based social network. To do that, we use a dataset obtained from Apontador, a Brazilian system with characteristics similar to Foursquare and Gowalla, where users share information about their locations and can navigate on existent system locations. As results, we identified models that describe unique characteristics of the user sessions on this kind of system, patterns in which requests arrive on the server as well as the access profile of users in the system.

Keywords

Workload characterization Location-based social networks Web 2.0 

References

  1. Arlitt M (2000) Characterizing web user sessions. SIGMETRICS Perform Eval Rev 28(2):50–63CrossRefGoogle Scholar
  2. Arlitt M, Jin T (1999) Workload characterization of the 1998 world cup web site. In: Technical Report HPL-1999-35R1Google Scholar
  3. Arlitt M, Krishnamurthy D, Rolia J (2001) Characterizing the scalability of a large web-based shopping system. In ACM Trans Internet Technol, pp 44–69Google Scholar
  4. Arlitt M, Williamson C (1996) Web server workload characterization: the search for invariants. SIGMETRICS Perform Eval Rev 24(1):126–137CrossRefGoogle Scholar
  5. Barford P, Bestavros A, Bradley A, Crovella M (1999) Changes in Web client access patterns: characteristics and caching implications. In: Proceedings of international conference on World Wide Web (WWW), pp 15–28Google Scholar
  6. Barford P, Crovella M (1998) Generating representative web workloads for network and server performance evaluation. ACM SIGMETRICS Jt Intern Conf Measure Model Comput Syst 26:151–160Google Scholar
  7. Benevenuto F, Duarte F, Almeida V, Almeida J (2005) Web Cache replacement policies: properties, limitations and implications. In: Proceedings of Latin American Web Congress (La-Web)Google Scholar
  8. Benevenuto F, Pereira A, Rodrigues T, Almeida V, Almeida J, Gontalves M (2010) Characterization and analysis of user profiles in online video sharing systems. J Info Data Manag 1(2):115–129Google Scholar
  9. Benevenuto F, Rodrigues T, Cha M, Almeida V (2009) Characterizing user behavior in online social networks. In: ACM SIGCOMM conference on Internet measurement conference (IMC), pp 49–62Google Scholar
  10. Benevenuto F, Rodrigues T, Cha M, Almeida V (2012) Characterizing user navigation and interactions in online social networks. Inform Sci 195(15):1–24CrossRefGoogle Scholar
  11. Carrera D, Gavalda R, Torres J, Ayguade E (2010) Characterization of workload and resource consumption for an online travel and booking site. In: Proceedings of IEEE international symposium on workload characterization (IISWC), pp 1–10Google Scholar
  12. Cha M, Kwak H, Rodriguez P, Ahn Y, Moon S (2007) I Tube, You Tube, Everybody Tubes: analyzing the world’s largest user generated content video system. In: ACM Internet Measurement ConferenceGoogle Scholar
  13. Cho E, Myers S, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1082–1090Google Scholar
  14. Comarela G, Crovella M, Almeida V, Benevenuto F (2012) Understanding factors that affect response rates in twitter. In: Proceedings of the ACM conference on hypertext and social media (HT), pp 123–132Google Scholar
  15. Inc. comScore (2011) Nearly 1 in 5 smartphone owners access check-in services via their mobile device. http://bit.ly/mgaCIG
  16. Costa C, Cunha I, Vieira A, Ramos C, Rocha M, Almeida J, Ribeiro-Neto B (2004) Analyzing client interactivity in streaming media. In: World Wide Web Conference (WWW), pp 534–543Google Scholar
  17. Duarte F, Mattos B, Bestavros A, Almeida V, Almeida J (2007) Traffic characteristics and communication patterns in blogosphere. In Proceedings international conference on weblogs and social media (ICWSM)Google Scholar
  18. Erramillia V, Yanga X, Rodriguez P (2012) Explore what-if scenarios with song: social network write generator. http://arxiv.org/abs/1102.0699
  19. Fan L, Cao P, Almeida J, Broder A (2000) Summary cache: a scalable wide-area web cache sharing protocol. IEEE / ACM Trans Netw 8(3):281–293CrossRefGoogle Scholar
  20. Gavras A, Karila A, Fdida S, May M, Potts M (2007) Future internet research and experimentation: the fire initiative. SIGCOMM Comput Commun Rev 37:89–92CrossRefGoogle Scholar
  21. Gill P, Arlitt M, Li Z, Mahanti A (2007) Youtube traffic characterization: a view from the edge. In: ACM SIGCOMM conference on internet measurement (IMC)Google Scholar
  22. Gill P, Arlitt M, Li Z, Mahanti A (2008) Characterizing user sessions on youtube, In: IEEE Multimedia Computing and Networking (MMCN)Google Scholar
  23. Khan A, Yan X, Shu T, Anerousis N (2012) Workload characterization and prediction in the cloud: a multiple time series approach. In: IEEE network operations and management symposium (NOMS), pp 1287–1294Google Scholar
  24. Krishnamurthy D, Rolia J, Majumdar S (2006) A synthetic workload generation technique for stress testing session-based systems. IEEE Trans Softw Eng 32:868–882CrossRefGoogle Scholar
  25. Key Facts (2012) Facebook Newsroom. http://newsroom.fb.com/Key-Facts
  26. Menascé D, Almeida V (2000) Scaling for E business: technologies, models, performance, and capacity planning. Prentice Hall PTR, Upper Saddle RiverGoogle Scholar
  27. Menascé D, Almeida V, Fonseca R, Mendes M (1999) A methodology for workload characterization of e-commerce sites. In: ACM conference on electronic commerce (EC)Google Scholar
  28. Needle in a Haystack (2009) Efficient storage of Billions of Photos, Facebook Engineering Notes, http://tinyurl.com/cju2og
  29. Noulas A, Mascolo C, Scellato S, Pontil M (2011) Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. SMW 2011Google Scholar
  30. Noulas A, Scellato S, Mascolo C, Pontil M (2011) An empirical study of geographic user activity patterns in foursquare. In: International conference on weblogs and social mediaGoogle Scholar
  31. Oke Ad, Bunt R (2002) Hierarchical workload characterization for a busy web server. In: International conference on computer performance evaluation, modelling techniques and tools (TOOLS)Google Scholar
  32. Pereira A, Silva L, Meira Jr W (2006) Evaluating the impact of reactive workloads on the performance of web applications. In: Proceedings of the 25th IEEE international performance, computing, and communications ccnference (IPCCC), Phoenix, Arizona, IEEE CSGoogle Scholar
  33. Rodrigues T, Benevenuto F, Cha M, Gummadi K, Almeida V (2011) On word-of-mouth based discovery of the web. In: ACM SIGCOMM internet measurement conference (IMC), pp 381–393Google Scholar
  34. Scellato S (2011) Beyond the social web: the geo-social revolution. SIGWEB Newslett, pp 5:1–5:5Google Scholar
  35. Scellato S, Mascolo C, Musolesi M, Crowcroft J (2011) Track globally, deliver locally: Improving content delivery networks by tracking geographic social cascades. In: Proceedings of international conference on world wide web (WWW), pp 457–466Google Scholar
  36. Schneider F, Feldmann A, Krishnamurthy B, Willinger W (2009) Understanding online social network usage from a network perspective. In: ACM SIGCOMM Internet Measurement Conference (IMC), pp 35–48Google Scholar
  37. Vasconcelos M, Ricci S, Almeida J, Benevenuto F, Almeida V (2012) Caracterizacpo e influOncia do uso de tips e dones no foursquare. In: Simp=sio Brasileiro de Redes de Computadores e Sistemas Distribufdos (SBRC)Google Scholar
  38. Vasconcelos M, Ricci S, Almeida J, Benevenuto F, Almeida V (2012) Tips, dones and to-dos: uncovering user profiles in foursquare. In: ACM international conference of web search and data mining (WSDM)Google Scholar
  39. Veloso E, Almeida V, Meira W Jr, Bestavros A, Jin S (2006) A hierarchical characterization of a live streaming media workload. IEEE/ACM Trans Netw 14(1):133–146CrossRefGoogle Scholar
  40. Wang J (1999) A survey of web caching schemes for the internet. ACM Comput Commun Rev 25(9):36–46CrossRefGoogle Scholar
  41. Wittie M, Pejovic V, Deek L, Almeroth K, Zhao B (2010) Exploiting locality of interest in online social networks. In: Proceedings of ACM international conference on emerging networking experiments and technologies (CoNEXT), pp 1–12Google Scholar
  42. Xi H, Zhan J, Jia Z, Hong X, Wang L, Zhang L, Sun N, Lu G (2011) Characterization of real workloads of web search engines. In: Proceedings of IEEE international symposium on workload characterization (IISWC), pp 15–25Google Scholar
  43. YouTube Fact Sheet (2011) http://www.youtube.com/t/fact_sheet Acessado em Dezembro/2012

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Theo Lins
    • 1
  • Adriano C. M. Pereira
    • 1
  • Fabrício Benevenuto
    • 1
  1. 1.Computer Science Department (DCC)Federal University of Minas Gerais (UFMG)Belo HorizonteBrazil

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