We present a multi-agent system on top of the IaaS layer consisting of a scheduler agent and multiple worker agents. Each job is controlled by an autonomous worker agent, which is equipped with application specific knowledge (e.g., performance functions) allowing it to estimate the type and number of necessary resources. During runtime, the worker agent monitors the job and adapts its resources to ensure the specified quality of service -- even in noisy clouds where the job instances are influenced by other jobs. All worker agents interact with the scheduler agent, which takes care of limited resources and does a cost-aware scheduling by assigning jobs to times with low energy costs. The whole architecture is self-optimizing and able to use public or private clouds.