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With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self-Tuning Particle Swarm Optimization approach(called STPSO) is proposed to configure the cloud environment. STPSO can automatically adjust the arbitrary configuration to a valid configuration. To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.
With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self- To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows (referred to as STPSO) is automatically configured to configure the cloud environment. that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.