Abstract: Methods, systems, and computer readable media for testing a system under test (SUT). An example system includes a distributed processing node emulator configured for emulating a multi-processing node distributed computing system using a processing node communications model and generating intra-processing node communications and inter-processing node communications in the multi-processing node distributed computing system. At least a portion of the inter-processing node communications comprises one or more messages communicated with the SUT by way of a switching fabric. The system includes a test execution manager configured for managing the distributed processing node emulator to execute a pre-defined test case, monitoring the SUT, and outputting a test report based on monitoring the SUT during execution of the pre-defined test case.

Abstract: Methods, systems, and computer readable media for testing a system under test (SUT). An example system includes a distributed processing node emulator configured for emulating a multi-processing node distributed computing system using a processing node communications model and generating intra-processing node communications and inter-processing node communications in the multi-processing node distributed computing system. At least a portion of the inter-processing node communications comprises one or more messages communicated with the SUT by way of a switching fabric. The system includes a test execution manager configured for managing the distributed processing node emulator to execute a pre-defined test case, monitoring the SUT, and outputting a test report based on monitoring the SUT during execution of the pre-defined test case.

Abstract: Methods, systems, and computer readable media for network traffic generation using machine learning. An example method includes collecting first traffic from a production data center environment. At least a portion of the first traffic comprises live computer network traffic transiting the production data center environment. The method includes collecting second traffic from an emulated data center testbed device. At least a portion of the second traffic comprises testbed traffic that transits an emulated data center switching fabric of the emulated data center testbed device. The method includes training a traffic generation inference engine using the first traffic and the second traffic. The method includes generating, using the traffic generation inference engine, test traffic to test or stimulate a network system under test (SUT).

Abstract: Methods, systems, and computer readable media for network traffic generation using machine learning. An example method includes collecting first traffic from a production data center environment. At least a portion of the first traffic comprises live computer network traffic transiting the production data center environment. The method includes collecting second traffic from an emulated data center testbed device. At least a portion of the second traffic comprises testbed traffic that transits an emulated data center switching fabric of the emulated data center testbed device. The method includes training a traffic generation inference engine using the first traffic and the second traffic. The method includes generating, using the traffic generation inference engine, test traffic to test or stimulate a network system under test (SUT).

Abstract: The subject matter described herein includes methods, systems, and computer readable media for stateless service traffic generation. A method for stateless service traffic generation occurs at a network equipment test system. The method includes generating, at a first transmit port associated with the network equipment test system, a first test packet flow comprising one or more packets, wherein the first test packet flow indicates a match and action instruction for triggering an action at a second transmit port associated with the network equipment test system; sending the first test packet flow toward a node associated with a data center under test (DCUT); receiving the first test packet flow from the node associated with the DCUT; and performing, using the match and action instruction, the action at the second transmit port associated with the network equipment test system.

Abstract: Methods, systems, and computer readable media for network traffic generation using machine learning. An example method includes collecting first traffic from a production data center environment. At least a portion of the first traffic comprises live computer network traffic transiting the production data center environment. The method includes collecting second traffic from an emulated data center testbed device. At least a portion of the second traffic comprises testbed traffic that transits an emulated data center switching fabric of the emulated data center testbed device. The method includes training a traffic generation inference engine using the first traffic and the second traffic. The method includes generating, using the traffic generation inference engine, test traffic to test or stimulate a network system under test (SUT).

Abstract: The subject matter described herein includes methods, systems, and computer readable media for stateless service traffic generation. A method for stateless service traffic generation occurs at a network equipment test system. The method includes generating, at a first transmit port associated with the network equipment test system, a first test packet flow comprising one or more packets, wherein the first test packet flow indicates a match and action instruction for triggering an action at a second transmit port associated with the network equipment test system; sending the first test packet flow toward a node associated with a data center under test (DCUT); receiving the first test packet flow from the node associated with the DCUT; and performing, using the match and action instruction, the action at the second transmit port associated with the network equipment test system.