SimSim: A Service Discovery Method Preserving Content Similarity and Spatial Similarity in P2P Mobile Cloud

Zhiming Cai, Ivan Lee, Shu-Chuan Chu, Xuehong Huang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Mobile cloud has become a new computing paradigm such that services are accessible in any place and at any time. Despite its promising prospect, challenges arise due to unreliable channel condition and limited bandwidth in wireless communication, dynamic route establishment due to node mobility, difficulties in associating request to relevant service providers, and complication in service deployment. To ensure the fairness of resource allocation and network load balance, it is necessary to consider strategies for distributing services. In this paper, we propose SimSim, a service discovery scheme based on keywords search which preserves content similarity and spatial similarity. A mapping from a keyword set of services to a bit vector with identical hash is designed to preserve content similarity. The proposed technique applies a hierarchical hash clustering model and investigates the strategies of service deployment and discovery. By mapping the services characterized by keywords to the Gray space, SimSim offers similar services at close geographical proximity. Extensive simulations have been conducted to assess the proposed system.

Original languageEnglish
Pages (from-to)79-95
Number of pages17
JournalJournal of Grid Computing
Issue number1
Publication statusPublished - 17 Jan 2019


  • Service discovery
  • gray space
  • content similarity
  • spatial similarity
  • hierarchical hash clustering
  • Gray space
  • Spatial similarity
  • Hierarchical hash clustering
  • Content similarity


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