Abstract: A container set manager may determine a plurality of container sets, each container set specifying a non-functional architectural concern associated with deployment of a service within at least one data center. A decision table manager may determine a decision table specifying relative priority levels of the container sets relative to one another with respect to the deployment. A placement engine may determine an instance of an application placement model (APM), based on the plurality of container sets and the decision table, determine an instance of a data center placement model (DPM) representing the at least one data center, and generate a placement plan for the deployment, based on the APM instance and the DPM instance.

Abstract: The system may include a troubleshooting activity recorder configured to record troubleshooting sessions. Each troubleshooting session may include a sequence of queries and query results. The troubleshooting activity recorder may include a query transformer configured to transform the queries and the query results into transformed queries and transformed query results before recording the troubleshooting sessions. The troubleshooting activity recorder may be configured to record the transformed queries and the transformed query results as troubleshooting session information in a troubleshooting activity database. The system may include a troubleshooting pattern learning unit including a graph builder configured to generate a troubleshooting pattern graph having query nodes and links between the query nodes based on the troubleshooting session information.

Abstract: A computing system determines incremental values associated with a plurality of clustering solutions. Each of the clustering solutions groups stores of a retailer into clusters in a different way. For each clustering solution in the plurality of clustering solutions, the incremental value associated with the clustering solution indicates a difference between an estimated revenue associated with the clustering solution and revenue associated with a baseline clustering solution. The computing system then determines, based on the incremental values associated with the plurality of clustering solutions, the appropriate number of clusters. The clustering solutions that group the stores into more or fewer clusters than the appropriate number of clusters tend to be associated with incremental values that are the same or lower than the clustering solutions that group the stores into the appropriate number of clusters.

Abstract: Customer data is retrieved that includes customer transactions of a retailer for a first time period and a second time period prior to the first time period. Each customer transaction may be associated with an identified customer. Customer transactions that occurred during the second time period and that are associated with an identified customer that was identified after the second time period are removed from the customer data. For each identified customer, a customer identification weighting factor is determined based on a customer identification likelihood. A total number of customer transactions associated with the identified customer is multiplied by the identification weighting factor to determine a weighted total number of customer transactions for the identified customer. The weighted total number of customer transactions for the identified customer is associated with the identified customer in the customer data. A report including a representation of the customer data is output.

Abstract: According to one general aspect, a method of semi-automatically discovering and generating useful service blueprints may include collecting, by an apparatus, a plurality of configuration information sets regarding a plurality of network service applications. The method may also include converting, by the apparatus, the plurality of configuration information sets into one or more normalized application instance graphs. The method may further include generating, by the apparatus, one or more application blueprint files based, at least in part, upon the one or more normalized application instance graphs.

Abstract: The embodiments provide a data processing apparatus for automated blueprint assembly. The data processing apparatus includes a micro-blueprint assembler configured to receive a request for automated blueprint assembly for assembling an application, where the request specifies at least one feature, and a model database configured to store model data. The model data includes a plurality of classes and class properties. The data processing apparatus further includes a micro-blueprint database configured to store a plurality of micro-blueprints. Each micro-blueprint corresponds to a functional component of a stack element or service tier, and the functional component is annotated with one or more classes of the plurality of classes and at least one required capability and available capability. The micro-blueprint assembler is configured to generate at least one application blueprint based on the model data and the plurality of micro-blueprints according to the request.

Abstract: A centralized, policy-driven approach allows dynamic self-configuration and self-deployment of large scale, complex, heterogeneous monitoring agent networks. Such an approach resolves the scalability and manageability issues of manually configured conventional agents. Embodiments of the agents can be self-configuring using a dynamic, adaptive technique. An administrator can group hosts on which agents into groups that have similarly configured agents.

Abstract: Disclosed are systems and methods for model based provisioning of applications and servers (both physical and virtual) to execute provisioned applications in a reliable and repeatable manner. Several aspects of a complex application management including compliance, change tracking, monitoring, discovery, processing steps, CMDB integration are disclosed within a comprehensive hierarchy of definition templates forming a model. This model can then be used at provisioning time to instantiate a compliant instance of the provisioned application. This model can also be used at run-time for managing run-time aspects of the provisioned application. Additionally, the model based approach can help track applications even when or if applications drift from their intended design and policies for use.