Abstract: The present teaching relates to generating combination distributions for ads. Features are computed based on training data associated with ads, each of which has a plurality of attributes. The training data include asset combinations with past performance thereof for each of the ads. Each combination includes multiple assets representing respective attributes of an ad. The features are used in machine learning to obtain an auxiliary model, which is used to generate combination distributions for each ad based on predicted performance for each combination associated with the ad. Such generated combination distributions are sent to an explore/exploit layer (EEL) for a frontend ad serving engine to draw a combination therefrom for an auction winning ad for rendering on a webpage viewed by a user on a user device.

Abstract: The present teaching relates to displaying ads. An explore/exploit layer (EEL) is provided at frontend ad serving engine for storing combination distributions with respect to multiple ads. Each ad has multiple attributes. Each attribute can be instantiated using one of multiple assets. The frontend ad serving engine requests a recommended ad for bidding an ad display opportunity in a slot of a webpage viewed by a user on a user device. The recommended ad is one of the multiple ads. When the auction is successful, a combination of assets for the ad is drawn from the combination distributions in EEL and each of the assets instantiates a corresponding attribute of the ad. The combination is transmitted to the user device to render the ad.

Abstract: The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.

Abstract: Example implementations relate to isolating production environment debugging sessions. Some example implementations may include a runtime execution engine to execute, using a production environment, an original work flow corresponding to a unit of production environment source code. Some example implementations may also include a session initiation request engine to receive a request to perform a debugging session of a modified version of the unit of production environment source code. In some examples, the request may include a modified execution plan corresponding to a machine-readable translation of the modified version. Some example implementations may also include a debugging execution engine to execute the modified execution plan in isolation, the modified execution plan being executed in the production environment without altering at least one of the unit of production environment source code and the original work flow.

Abstract: A system and method are disclosed for automating the deployment of a multi-component computer environment. A user interface module (102) is configured to enable a user to design a desired topology of components in the multi-component computer environment. A deployment processing module (106) is configured to convert the desired topology to a set of deployment actions represented in a workflow script. An orchestration engine (114) is configured to use the set of deployment actions in the workflow script to execute an automated deployment of the multi-component computer environment on at least one deployment machine (118) to create the multi-component computer environment having the desired topology.

Abstract: A system and method are disclosed for automating the deployment of a multi-component computer environment. A user interface module (102) is configured to enable a user to design a desired topology of components in the multi-component computer environment. A deployment processing module (106) is configured to convert the desired topology to a set of deployment actions represented in a workflow script. An orchestration engine (114) is configured to use the set of deployment actions in the workflow script to execute an automated deployment of the multi-component computer environment on at least one deployment machine (118) to create the multi-component computer environment having the desired topology.

Abstract: A method is provided for deploying an application component in a multi-component deployment configuration. An application component and integration topology is acquired for a multi-component deployment configuration. An additional application component is selected. A predefined integration is enabled associating the additional application component with a deployed application component in the application component and integration topology. The additional application component and the predefined integration are deployed on to a target machine.