Title: Sekitei: An AI planner for Constrained Component Deployment in Wide-Area Networks (NYU-CS-TR851) Author: Tatiana Kichkaylo, Anca Ivan, and Vijay Karamcheti Abstract: Wide-area network applications are increasingly being built using component-based models, enabling integration of diverse functionality in modules distributed across the network. In such models, dynamic component selection and deployment enables an application to flexibly adapt to changing client and network characteristics, achieve loadbalancing, and satisfy QoS requirements. Unfortunately, the problem of finding a valid component deployment is hard because one needs to decide on the set of components while satisfying various constraints resulting from application semantic requirements, network resource limitations, and interactions between the two. In this paper, we describe a general model for the component placement problem and present an algorithm for it, which is based on AI planning algorithms. We validate the effectiveness of our algorithm by demonstrating its scalability with respect to network size and number of components in the context of deployments generated for two example applications a security-sensitive mail service, and a webcast service in a variety of network environments.