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% of cloud-related costs and can achieve answers at least seven times faster in comparison to recent studies.
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