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.

中文翻译：

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SimSim：一种在P2P移动云中保留内容相似性和空间相似性的服务发现方法

移动云已成为一种新的计算范例，可以在任何地方，任何时间访问服务。尽管前景广阔，但由于信道条件不可靠和无线通信中的带宽有限，由于节点移动性而导致的动态路由建立，将请求关联到相关服务提供商的困难以及服务部署的复杂性，都带来了挑战。为了确保资源分配和网络负载平衡的公平性，有必要考虑分配服务的策略。在本文中，我们提出了SimSim，这是一种基于关键字搜索的服务发现方案，可以保留内容相似性和空间相似性。从服务的关键字集到具有相同散列的位向量的映射旨在保留内容相似性。所提出的技术应用了分层哈希聚类模型，并研究了服务部署和发现的策略。通过将以关键字为特征的服务映射到灰色空间，SimSim可以在地理上接近的位置提供类似的服务。已经进行了广泛的仿真来评估所提出的系统。