Relating the performance of partial-order planning algorithms to domain features
ACM SIGART Bulletin
Fast planning through planning graph analysis
Artificial Intelligence
CODASYL Data-Base Management Systems
ACM Computing Surveys (CSUR)
Deployment and Dynamic Reconfiguration Planning for Distributed Software Systems
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A Rule Based Approach to the Service Composition Life-Cycle
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
An intelligent assistant for interactive workflow composition
Proceedings of the 9th international conference on Intelligent user interfaces
Optimal Resource-Aware Deployment Planning for Component-Based Distributed Applications
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Total-order and partial-order planning: a comparative analysis
Journal of Artificial Intelligence Research
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
A survey of automated web service composition methods
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
Managing the configuration complexity of distributed applications in Internet data centers
IEEE Communications Magazine
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Today's enterprise data centers support thousands of mission-critical business applications composed of multiple distributed heterogeneous components. Application components exhibit complex dependencies on the configuration of multiple data center network, middleware, and related application resources. Applications are also associated with extended life-cycles, migrating from development to testing, staging and production environments, with frequent roll-backs. Maintaining end-to-end data center operational integrity and quality requires careful planning of (1) application deployment design, (2) resource selection, (3) provisioning operation selection, parameterization and ordering, and (4) provisioning operation execution. Current data center management products are focused on workflow-based automation of the deployment processes. Workflows are of limited value because they hard-code many aspects of the process, and are thus sensitive to topology changes. An emerging and promising class of model-based tools is providing new methods for designing detailed deployment topologies based on a set of requirements and constraints. In this paper we describe an approach to bridging the gap between generated “desired state” models and the elemental procedural provisioning operations supported by data center resources. In our approach, we represent the current and desired state of the data center using object models. We use AI planning to automatically generate workflows that bring the data center from its current state to the desired state. We discuss our optimizations to Partial Order Planning algorithms for the provisioning domain. We validated our approach by developing and integrating a prototype with a state of the art provisioning product. We also present initial results of a performance study.