In a data center, various components of Web applications co-located on virtualized servers exhibit complex timevarying 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

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... e complex systems. It applies machine learning based self-adaptive modeling to capture the complex and timevarying relationship between the application performance and allocation of resources to various application components, in the presence of highly dynamic and bursty workloads and interapplication 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.