Abstract: Disclosed aspects relate to cache management in a stream computing environment that uses a set of many-core hardware processors to process a stream of tuples by a plurality of processing elements which operate on the set of many-core hardware processors. The stream of tuples to be processed by the plurality of processing elements which operate on the set of many-core hardware processors may be received. A tuple-processing hardware-route on the set of many-core hardware processors may be determined based on a cache factor associated with the set of many-core hardware processors. The stream of tuples may be routed based on the tuple-processing hardware-route on the set of many-core hardware processors. The stream of tuples may be processed by the plurality of processing elements which operate on the set of many-core hardware processors.

Abstract: Provided are techniques for identification and handling of nested breakpoints during debug session. Program code having functions, nested breakpoints, and an indication of whether each of the breakpoints is likely to be reached is displayed, in a visualization. In response to receiving input that selects a function from the functions, a call graph is displayed for the functions in the program code to enable viewing where each of the nested breakpoints is located in the program code with reference to the functions. An option to disable a nested breakpoint of the nested breakpoints for a current execution of the program code is provided. In response to receiving input that selects the option, the nested breakpoint is disabled.

Abstract: Disclosed aspects relate to post-compilation configuration management in a stream computing environment to process a stream of tuples. An escalation request may be detected pertaining to a post-compilation phase in the stream computing environment. The escalation request may relate to a requested computing configuration for a process in the stream computing environment. An appropriate computing configuration may be determined for the process in the stream computing environment. The appropriate computing configuration may be determined based on the requested computing configuration for the process in the stream computing environment. The appropriate computing configuration may be established using a containerization technique for the process in the stream computing environment.

Abstract: A method, computer program product, and computer system for defining, on at least one processor, one or more potential overflow paths for processing at least a portion of a data load through a plurality of stream operators deployed on a plurality of computing devices. Embodiments may include determining, on the at least one processor, a conditional availability for each computing device of the plurality of computing devices. Embodiments may also include identifying, on the at least one processor, a change in one or more of a configuration of the plurality of computing devices and the at least a portion of the data load. Embodiments may further include dynamically activating, on the at least one processor, one or more potential overflow paths in response to the identified change based upon, at least in part, the conditional availability for each computing device of the plurality of computing devices.

Abstract: A method, apparatus and program product utilize Domain Name Service (DNS) prefetching in a recursive DNS server, e.g., to mitigate Distributed Denial of Service (DDoS) attacks on a DNS service.

Abstract: A method, apparatus and program product utilize Domain Name Service (DNS) prefetching in a recursive DNS server, e.g., to mitigate Distributed Denial of Service (DDoS) attacks on a DNS service.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to resource usage management in a stream computing environment that includes a set of hosts having a set of processing elements which has a set of stream operators. A first activity indicator may be detected for a first subset of the set of stream operators. A resource action for performance with respect to the first subset of the set of stream operators may be determined based on the first activity indicator. The resource action may be performed with respect to the first subset of the set of stream operators to benefit resource usage in the stream computing environment.

Abstract: A method, computer program product, and computer system for identifying, in a dynamically formed cloud, a plurality of voluntary compute nodes, wherein the plurality of voluntary compute nodes may include personal computing devices of a plurality of users, and wherein at least a portion of the plurality of voluntary compute nodes may be configured to communicate with each other to receive and send streaming data. A first compute node of at least the portion of the plurality of voluntary compute nodes may be identified to receive and distribute a streams operator of a streams graph requested by a client computing device. A second compute node of at least the portion of the plurality of voluntary compute nodes may be identified for verifying computational correctness of a tuple stream computation for the stream operator by replicating the tuple stream computations for the stream operator on the second compute node. A final result tuples of the graph may be sent to the client computing device.

Abstract: System resource usage by a streaming application processing workloads can be monitored. Based on the monitoring, at least one performance metric for the streaming application in processing the workloads can be determined. Based on the at least one performance metric, whether hosting of the streaming application is, or is near to, violating at least one criteria specified by a service level agreement for the streaming application can be determined. If so, at least one additional instance of at least one parallelizable operator of the streaming application that is currently executing can be initialized. Responsive to initializing the at least one additional instance of at least one of the parallelizable operators, a portion of the workloads can be directed to the at least one additional instance of at least one of the parallelizable operators.

Abstract: Provided are techniques for identification and handling of nested breakpoints during debug session. Program code having functions, nested breakpoints, and an indication of whether each of the breakpoints is likely to be reached is displayed, in a visualization. In response to receiving input that selects a function from the functions, a call graph is displayed for the functions in the program code to enable viewing where each of the nested breakpoints is located in the program code with reference to the functions. An option to disable a nested breakpoint of the nested breakpoints for a current execution of the program code is provided. In response to receiving input that selects the option, the nested breakpoint is disabled.

Abstract: Disclosed aspects relate to cache management in a stream computing environment that uses a set of many-core hardware processors to process a stream of tuples by a plurality of processing elements which operate on the set of many-core hardware processors. The stream of tuples to be processed by the plurality of processing elements which operate on the set of many-core hardware processors may be received. A tuple-processing hardware-route on the set of many-core hardware processors may be determined based on a cache factor associated with the set of many-core hardware processors. The stream of tuples may be routed based on the tuple-processing hardware-route on the set of many-core hardware processors. The stream of tuples may be processed by the plurality of processing elements which operate on the set of many-core hardware processors.

Abstract: Disclosed aspects relate to cache management in a stream computing environment that uses a set of many-core hardware processors to process a stream of tuples by a plurality of processing elements which operate on the set of many-core hardware processors. The stream of tuples to be processed by the plurality of processing elements which operate on the set of many-core hardware processors may be received. A tuple-processing hardware-route on the set of many-core hardware processors may be determined based on a cache factor associated with the set of many-core hardware processors. The stream of tuples may be routed based on the tuple-processing hardware-route on the set of many-core hardware processors. The stream of tuples may be processed by the plurality of processing elements which operate on the set of many-core hardware processors.

Abstract: Disclosed aspects relate to cache management in a stream computing environment that uses a set of many-core hardware processors to process a stream of tuples by a plurality of processing elements which operate on the set of many-core hardware processors. The stream of tuples to be processed by the plurality of processing elements which operate on the set of many-core hardware processors may be received. A tuple-processing hardware-route on the set of many-core hardware processors may be determined based on a cache factor associated with the set of many-core hardware processors. The stream of tuples may be routed based on the tuple-processing hardware-route on the set of many-core hardware processors. The stream of tuples may be processed by the plurality of processing elements which operate on the set of many-core hardware processors.

Abstract: A method, computer program product, and computer system for maintaining, at a computing device, one or more tuples in a software-level queue. The one or more tuples may be transported as a batch of the one or more tuples from the software-level queue to a first queue for processing at a hardware accelerator. After processing the one or more tuples, the one or more tuples may be transported from the first queue to a second queue at the hardware accelerator. The one or more tuples may be transported from the second queue to a next location.

Abstract: A method, computer program product, and computer system for maintaining, at a computing device, one or more tuples in a software-level queue. The one or more tuples may be transported as a batch of the one or more tuples from the software-level queue to a first queue for processing at a hardware accelerator. After processing the one or more tuples, the one or more tuples may be transported from the first queue to a second queue at the hardware accelerator. The one or more tuples may be transported from the second queue to a next location.

Abstract: A mechanism is provided for automated management of a set of media data using a set of social networking data. The set of social networking data related to a media event is collected. The set of social networking data related to the media event is correlated with the set of media data of the media event. Based on the set of social networking data, a determination is made of an inflection point of the media event. An inflection marker for the inflection point of the media event is then established with respect to the set of media data of the media event.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a first data frame and a second data frame by a device driver of a first virtual network interface controller (vNIC) of a plurality of vNICs sharing a physical network interface controller (NIC) of a computing system, storing, by the device driver of the first vNIC, the first and second data frames in a first queue for the first vNIC, generating, by the device driver of the first vNIC, an optimized data frame comprising the first and second data frames, wherein the optimized data frame specifies a modified Ethernet preamble, and sending the optimized data frame to a hypervisor of the computing system.