Abstract: One method occurs at an impairment test system.

Abstract: The subject matter described herein includes methods, systems, and computer readable media for non-intrusive queue analysis. A method for non-intrusive queue analysis occurs at a test analyzer of a test system, the test analyzer for analyzing packet queue performance of a system under test (SUT). The method comprising: receiving, via at least one mirrored ingress port of the SUT, a plurality of copied ingress test packets; receiving, via at least one mirrored egress port of the SUT, a plurality of copied egress test packets; correlating, using a correlation technique, the plurality of copied ingress test packets and the plurality of copied egress test packets; and generating, using the correlated packets, at least one packet queue performance metric associated with the SUT.

Abstract: Methods, systems, and computer readable media for emulating and testing data flows in distributed computing systems. An example system includes a workload abstractor configured for receiving monitored traffic in a distributed computing system performing a machine learning task and generating, using the monitored traffic, a test environment-agnostic workload model for the machine learning task and storing the test environment-agnostic workload model in a workload model repository with one or more other workload models. The system includes a test controller configured for selecting a test case for the machine learning task and a testbed mode for the test case; executing the test case by translating the test environment-agnostic workload model into a testbed-specific workload model for the testbed mode; and reporting, based on executing the test case, one or more performance metrics for the machine learning task.

Abstract: The subject matter described herein includes methods, systems, and computer readable media for non-intrusive queue analysis. A method for non-intrusive queue analysis occurs at a test analyzer of a test system, the test analyzer for analyzing packet queue performance of a system under test (SUT). The method comprising: receiving, via at least one mirrored ingress port of the SUT, a plurality of copied ingress test packets; receiving, via at least one mirrored egress port of the SUT, a plurality of copied egress test packets; correlating, using a correlation technique, the plurality of copied ingress test packets and the plurality of copied egress test packets; and generating, using the correlated packets, at least one packet queue performance metric associated with the SUT.

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: One example method occurs at a test system implemented using at least one processor, the method comprising: sending, via an application programming interface (API) and to a first traffic generator, a first instruction for setting a rate of background test packets sent to or via a system under test (SUT) for a test session; sending the background test packets to or via the SUT during the test session; receiving, from at least one feedback entity, feedback indicating at least one traffic metric associated with the background test packets sent to or via the SUT during the test session; generating, using the feedback, a second instruction for adjusting the rate of background test packets sent during the test session; and providing, via the API and to the first traffic generator, the second instruction for adjusting the rate of background test packets sent to or via the SUT during the test session.

Abstract: One example method occurs at a test system implemented using at least one processor, the method comprising: sending, via an application programming interface (API) and to a first traffic generator, a first instruction for setting a rate of background test packets sent to or via a system under test (SUT) for a test session; sending the background test packets to or via the SUT during the test session; receiving, from at least one feedback entity, feedback indicating at least one traffic metric associated with the background test packets sent to or via the SUT during the test session; generating, using the feedback, a second instruction for adjusting the rate of background test packets sent during the test session; and providing, via the API and to the first traffic generator, the second instruction for adjusting the rate of background test packets sent to or via the SUT during the test session.

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: A method for configuring a network service system for performing a network service using AR comprises: at a user device executing an AR cabling application: communicating, to an AR cabling subsystem of the network service system, network service task identification information usable for identifying at least one testing or monitoring case (TMC) definition provisioned within the network service system; receiving, from the AR cabling subsystem of the network service system, cabling instructions based on the network service task identification information, wherein the cabling instructions is for instructing the user to perform a cabling task associated with the at least one TMC definition, wherein the cabling task involves connecting at least one cable to one or more physical ports of a physical resource of the network service system; and providing, via a display and using at least one AR element, the cabling instructions for instructing the user to perform the cabling task.

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: One method occurs at a test controller of a network test system implemented using at least one processor.

Abstract: One method occurs at an impairment test system.

Abstract: One method occurs at a test controller of a network test system implemented using at least one processor.

Abstract: According to one method, the method occurs at a test system implemented using at least one processor. The method includes receiving test configuration information associated with a test session for configuring a test infrastructure connecting at least one test application and a system under test (SUT), wherein the test infrastructure includes at least two CTI devices that are dynamically configurable to perform one or more test related functions; configuring, using test configuration information, the test infrastructure to handle traffic for the test session; initiating the test session, wherein the test session involves using the at least two CTI devices and the at least one test application to test the SUT; and obtaining and reporting test results associated with the test session.

Abstract: A method for configuring a network service system for performing a network service using AR comprises: at a user device executing an AR cabling application: communicating, to an AR cabling subsystem of the network service system, network service task identification information usable for identifying at least one testing or monitoring case (TMC) definition provisioned within the network service system; receiving, from the AR cabling subsystem of the network service system, cabling instructions based on the network service task identification information, wherein the cabling instructions is for instructing the user to perform a cabling task associated with the at least one TMC definition, wherein the cabling task involves connecting at least one cable to one or more physical ports of a physical resource of the network service system; and providing, via a display and using at least one AR element, the cabling instructions for instructing the user to perform the cabling task.

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: A method for dynamic firewall configuration for accessing service hosted in virtual networks includes monitoring, in a virtual network, changes in an Internet protocol (IP) address of a service hosted in a virtual network. The method further includes detecting a change the IP address of the service hosted in the virtual network. The method further includes communicating notification of the change in IP address to a firewall policy management interface. The method further includes, automatically configuring a firewall to allow access to the service hosted in the virtual network.