Abstract
Dynamic provisioning of computational resources in cloud platforms is a challenging problem. In this scenario, multiagent system technology may offer noticeable improvements, with intelligent agents to dynamically choose the best computational load distribution reducing execution time and cloud services cost. Thus, this article presents MAS-Cloud, a multiagent system to dynamically monitor, predict and provide computational resources in cloud platforms. Deductive reasoning agents work cooperatively in a three layer architecture to provide transparent horizontal elasticity of virtual machines in public cloud platforms (i.e., Google, Amazon EC2). MAS-Cloud was evaluated with a nondeterministic and CPU intensive application providing a challenging validation case. A multiple linear regression model was used presenting good predictions with reduced mean absolute percent error achieving 3.59% CPU usage and 5.28% of execution time in the Google platform, and 3.97% CPU usage and 5.47% of execution time in the Amazon EC2 platform.
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