Abstract: According to examples, a system for providing dynamic digital content may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive a plurality of data feeds. The processor may further analyze the data feeds to identify values for parameterized variables. A plurality of deep learning (DL) models can be trained to obtain product attribute data from the data feeds. The processor may then identify rules or triggers based on the values of the parameterized variables. The rules and/or triggers cause the processor to dynamically generate or select digital content and transmit the digital content to user communication devices of selected audience.

Abstract: A computer-implemented method, in an example, may include calculating a time-to-live value for at least one packet based on a hop count between each of a plurality of processing element containers within an application streaming network; updating at least one processing element container output connection based on the calculated time-to-live value; and monitoring a streams resource metrics service for a change in a packet delivery rate.

Abstract: Methods, computer program products, and systems are presented. A high availability system includes a high availability set of a primary node processing incoming computational tasks and two or more backup nodes in a queue. The incoming computational tasks for the high availability set is managed according to various policies. For example, based on detecting an impending failure of the primary due to a lack of resources allocated for the primary node, one of the backup node that has the resources lacking from the primary node and that is the least likely to be used for processing the incoming computational tasks is selected as a lender node, according to a borrowing policy, and the resources of the lender node is allocated to the primary for further processing of the incoming computational tasks.

Abstract: A method, system and computer program product are provided for implementing enhanced autocomplete via multiple mobile keyboards. A user input is mapped to other keys to identify a meaningful message. When a meaningful message is not identified, the user input is compared with text using other keyboards for the mobile device. When a meaningful message is identified, the user input is automatically corrected.

Abstract: A computer-implemented method, in an example, may include calculating a time-to-live value for at least one packet based on a hop count between each of a plurality of processing element containers within an application streaming network; updating at least one processing element container output connection based on the calculated time-to-live value; and monitoring a streams resource metrics service for a change in a packet delivery rate.

Abstract: A computer-implemented method, in an example, may include calculating a time-to-live value for at least one packet based on a hop count between each of a plurality of processing element containers within an application streaming network; updating at least one processing element container output connection based on the calculated time-to-live value; and monitoring a streams resource metrics service for a change in a packet delivery rate.

Abstract: A device detects that a first wearable device has connected to a network. The device receives user preferences from the first wearable device. The device identifies a set point corresponding to a climate parameter. The device receives climate information corresponding to the climate parameter from one or more sensors. The device determines whether the climate information is within a threshold percentage of the set point, and based on the determination that the climate information is not within the threshold percentage of the set point, the device adjusts the set point.

Abstract: A device detects that a first wearable device has connected to a network. The device receives user preferences from the first wearable device. The device identifies a set point corresponding to a climate parameter. The device receives climate information corresponding to the climate parameter from one or more sensors. The device determines whether the climate information is within a threshold percentage of the set point, and based on the determination that the climate information is not within the threshold percentage of the set point, the device adjusts the set point.

Abstract: A computer-implemented method, in an example, may include calculating a time-to-live value for at least one packet based on a hop count between each of a plurality of processing element containers within an application streaming network; updating at least one processing element container output connection based on the calculated time-to-live value; and monitoring a streams resource metrics service for a change in a packet delivery rate.

Abstract: Methods, computer program products, and systems are presented. A high availability system includes a high availability set of a primary node processing incoming computational tasks and two or more backup nodes in a queue. The incoming computational tasks for the high availability set is managed according to various policies. For example, based on detecting an impending failure of the primary due to a lack of resources allocated for the primary node, one of the backup node that has the resources lacking from the primary node and that is the least likely to be used for processing the incoming computational tasks is selected as a lender node, according to a borrowing policy, and the resources of the lender node is allocated to the primary for further processing of the incoming computational tasks.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: designating virtual machines including resources as respective node to service computational tasks directed to a high availability cloud architecture system; a primary node suffering from a lack of resources gets more resources as borrowed from other nodes that are present as a fail-safe of the primary node; and a resource usage log is kept to determine whether or not the primary node needs resources from other nodes later.

Abstract: Embodiments of the present invention provide a method, system and computer program product for targeted discounting of products based upon cost of operation. In an embodiment of the invention, a method of providing a notification includes predicting i) a first cost of usage for a first product and ii) a second cost of usage for a second product. The method also includes determining a range of discount for the first product based, at least in part, on a comparison of the first usage cost and the second usage cost, and determining a discount for the first product that is included in the range of discount for the first product. Finally, the method includes sending, to a user, a notification that includes the discount for the first product based on a prediction that the discount for the first product is likely to result in the user purchasing the first product.

Abstract: Disclosed aspects include managing asset deployment for a shared pool of configurable computing resources having a set of virtual machines associated with a deployment server. A set of deployment topology data is collected for the shared pool of configurable computing resources. Using the set of deployment topology data, it is determined to establish a dynamic management server communicatively connected with both the deployment server and the set of virtual machines. Based on the set of deployment topology data, the dynamic management server is established communicatively connected with both the deployment server and the set of virtual machines.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: designating virtual machines including resources as respective node to service computational tasks directed to a high availability cloud architecture system; a primary node suffering from a lack of resources gets more resources as borrowed from other nodes that are present as a fail-safe of the primary node; and a resource usage log is kept to determine whether or not the primary node needs resources from other nodes later.

Abstract: A device detects that a first wearable device has connected to a network. The device receives user preferences from the first wearable device. The device identifies a set point corresponding to a climate parameter. The device receives climate information corresponding to the climate parameter from one or more sensors. The device determines whether the climate information is within a threshold percentage of the set point, and based on the determination that the climate information is not within the threshold percentage of the set point, the device adjusts the set point.

Abstract: A device detects that a first wearable device has connected to a network. The device receives user preferences from the first wearable device. The device identifies a set point corresponding to a climate parameter. The device receives climate information corresponding to the climate parameter from one or more sensors. The device determines whether the climate information is within a threshold percentage of the set point, and based on the determination that the climate information is not within the threshold percentage of the set point, the device adjusts the set point.

Abstract: Usage data can be received by a computer system from a sensor-enabled appliance. A new appliance profile can be accessed from an external data source; the new appliance profile can contain price and energy consumption data about the new appliance. The system can also identify relational data, which can be collected from a set of sensor-enabled appliances. The relational data can be used to determine usage trends for the appliances. The usage data, new appliance profile, and relational data can then be used to determine that current usage costs exceed potential usage costs, and this determination can be transmitted to a user device.

Abstract: Disclosed aspects include managing asset deployment for a shared pool of configurable computing resources having a set of virtual machines associated with a deployment server. A set of deployment topology data is collected for the shared pool of configurable computing resources. Using the set of deployment topology data, it is determined to establish a dynamic management server communicatively connected with both the deployment server and the set of virtual machines. Based on the set of deployment topology data, the dynamic management server is established communicatively connected with both the deployment server and the set of virtual machines.

Abstract: Disclosed aspects include managing asset deployment for a shared pool of configurable computing resources having a set of virtual machines associated with a deployment server. A set of deployment topology data is collected for the shared pool of configurable computing resources. Using the set of deployment topology data, it is determined to establish a dynamic management server communicatively connected with both the deployment server and the set of virtual machines. Based on the set of deployment topology data, the dynamic management server is established communicatively connected with both the deployment server and the set of virtual machines.

Abstract: Disclosed aspects include managing asset deployment for a shared pool of configurable computing resources having a set of virtual machines associated with a deployment server. A set of deployment topology data is collected for the shared pool of configurable computing resources. Using the set of deployment topology data, it is determined to establish a dynamic management server communicatively connected with both the deployment server and the set of virtual machines. Based on the set of deployment topology data, the dynamic management server is established communicatively connected with both the deployment server and the set of virtual machines.