There are a vast number of cloud service providers, which offer virtual machines (VMs) with different configurations. From the companies perspective, an appropriate selection of VMs is an important issue, as the proper service selection leads to improved productivity, higher efficiency, and lower cost. An effective service selection cannot be done without a systematic approach due to the modularity of requests, the conflicts between requirements, and the impact of network parameters. In this paper, we introduce an innovative framework, called PCA, to solve service selection problem in the hybrid environment of peer-assisted, public, and private clouds. PCA detects the conflicts between the requests and enterprises policies, finds proper services based on the requirements, and reduces VMs rent and end-to-end network costs. PCA selects the services from multiple clouds to utilize resources and reduce the total cost. Our proposed framework utilizes set theory, B+ tree, and greedy algorithms to meet its goals. The simulation results show that PCA can reduce up to 30 percent of cloud-related costs and can achieve answers at least seven times faster in comparison to recent studies.

中文翻译：

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对等辅助环境中的网络和应用感知云服务选择

有大量的云服务提供商，它们提供具有不同配置的虚拟机（VM）。从公司的角度来看，正确选择虚拟机是一个重要的问题，因为正确的服务选择可以提高生产率，提高效率和降低成本。由于请求的模块化，需求之间的冲突以及网络参数的影响，没有系统的方法就无法进行有效的服务选择。在本文中，我们介绍了一个称为PCA的创新框架，以解决对等辅助，公共和私有云的混合环境中的服务选择问题。PCA可以检测到请求和企业策略之间的冲突，根据需求找到合适的服务，并降低VM的租金和端到端的网络成本。PCA从多个云中选择服务以利用资源并降低总成本。我们提出的框架利用集合论，B +树和贪婪算法来实现其目标。仿真结果表明，与最近的研究相比，PCA可以减少多达30％的与云相关的成本，并且可以至少快7倍地获得答案。