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In a data center, various components of Web applications co-located on virtualized servers exhibit complex time-varying interactions and interference. It has a significant impact on the user perceived performance and power consumption of the underlying system. We propose and develop APPLEware, an autonomic middleware for joint performance and power control of co-located Web applications. It features a distributed control structure that provides performance assurance and energy efficiency for large complex systems. It applies machine learning based self-adaptive modeling to capture the complex and time-varying relationship between the application performance and allocation of resources to various application components, in the presence of highly dynamic and bursty workloads and inter-application performance interference. The distributed controllers perform coordinated resource allocation to meet the service level agreements of applications in an agile and energy-efficient manner. Experimental results based on a testbed implementation with benchmark applications demonstrate APPLEware's effectiveness and energy efficiency.