Irregular community discovery for cloud service improvement

  • Authors:

  • Jin Liu;Jing Zhou;Junfeng Wang;Feng Zhang;Fei Liu

  • Affiliations:

  • State Key Lab. of Software Engineering, Wuhan University, Wuhan, China 430072 and Lab. of Complex Systems and Intelligence Science, Institute of Automation, CAS, Beijing, China 100190 and State Ke ...;School of Computer Science, Communication University of China, Beijing, China 100024 and Key Lab. of Intelligent Information Processing, Institute of Computing Technology, CAS, Beijing, China 1001 ...;College of Computer Science, Sichuan University, Chengdu, China 610064;Software School, Sun Yat-sen University, Guangzhou, China 510006;State Key Lab. of Software Engineering, Wuhan University, Wuhan, China 430072

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2012

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Abstract

Utility services provided by cloud computing rely on virtual customer communities forming spontaneously and evolving continuously. Clarifying the explicit boundaries of these communities is thus essential to the quality of utility services in cloud computing. Communities with overlapping features or prominent peripheral vertexes are usually typical irregular communities. Traditional community identification algorithms are limited in discovering irregular topological structures from CR networks, whereas these irregular shapes typically play an important role in finding prominent customers which are ignored in social CRM otherwise. We present a novel method of discovering irregular communities. It firstly finds and merges primitive maximal cliques and the irregular features of overlapping and prominent sparse vertices are further considered. An empirical case and a methodology comparison confirm the feasibility and efficiency of our approach.