Methods, systems, and devices supporting orchestration for automated performance testing are described. A server may orchestrate performance testing for software applications across multiple different test environments. The server may receive a performance test indicating an application to test and a set of test parameters. The server may determine a local or a non-local test environment for running the performance test. The server may deploy the application to the test environment, where the deploying involves deploying a first component of the performance test to a first test artifact in the test environment and deploying a second component of the performance test different from the first component to a second test artifact in the test environment. The server may execute the performance test to obtain a result set, where the executing involves executing multiple performance test components as well as orchestrating results across multiple test artifacts to obtain the result set.

Systems, methods and/or computer program products optimizing network policies between microservices of a service mesh. The service mesh tracks incoming API calls of applications and based on the historical transactions, the context of API calls, and the microservices in the microservice chain being invoked, network controls and policy configurations are set to optimize the transactions performed by the service mesh. Dimensions of the communications between microservices of the service mesh are dynamically optimized via the service mesh control plane using a policy optimizer. Optimized dimensions of service mesh transactions includes automated policy adjustments to retries between microservices, circuit breaking between microservices, automated timeout adjustments between microservices and intelligent rate limiting between microservices and/or rate limiting applied to user profiles.

A system for detecting malicious software, comprising at least one hardware processor adapted to: execute a tested software object in a plurality of computing environments each configured according to a different hardware and software configuration; monitor a plurality of computer actions performed in each of the plurality of computing environments when executing the tested software object; identify at least one difference between the plurality of computer actions performed in a first of the plurality of computing environments and the plurality of computer actions performed in a second of the plurality of computing environments; and instruct a presentation of an indication of the identified at least one difference on a hardware presentation unit.

A computer system is provided. The computer system includes a memory, a network interface, and at least one processor configured to monitor a user interface comprising a plurality of user interface elements; detect at least one changed element within the plurality of user interface elements; classify, in response to detecting the at least one changed element, the at least one changed element as either indicating or not indicating an error; generate, in response to classifying the at least one changed element as indicating an error, an error signature that identifies the at least one changed element; identify, using the error signature, a remediation for the error; and provide the remediation in association with the at least one changed element.

Methods, systems, apparatuses, and computer-readable storage mediums are described for machine learning-based techniques for reducing the visual complexity of a dependency graph that is representative of an application or service. For example, the dependency graph is generated that comprises a plurality of nodes and edges. Each node represents a compute resource (e.g., a microservice) of the application or service. Each edge represents a dependency between nodes coupled thereto. A machine learning-based classification model analyzes each of the nodes to determine a likelihood that each of the nodes is a problematic compute resource. For instance, the classification model may output a score indicative of the likelihood that a particular compute resource is problematic. The nodes and/or edges having a score that exceed a predetermined threshold are provided focus via the dependency graph.

An information processing system including Application Platform capable of communicating with Edge1 connected to each other to be able to communicate each other, in which Application Platform includes a second processor, information on microservices and data possessed by Edge1, and performance information describing the performance of Edge1, and the second processor uses predetermined data to combine a plurality of predetermined microservices and causes Edge1 to execute them in a predetermined order. When executing the application, microservices and data are moved between Edge1 based on the information of the microservices and the data possessed by Edge1, and the performance information.

Securely executing instructions of software on a computerized device by accessing a software of a computerized device, wherein the software includes a plurality of instructions and respective reference message authentication codes (MACs), generating a cryptographic key based at least in part on a key derivation function, wherein arguments of the key derivation function are based at least in part on a unique identifier of the computerized device and a value extended from a measurement of a content of the software of an extension mechanism of a platform configuration register of the computerized device, verifying an instruction of the plurality of instructions of the software based at least in part on the cryptographic key and a reference MAC of the respective reference MACs, and in response to verifying the instruction of the plurality of instructions of the software, executing the instruction.

A system is configured to obtain information relating to a current application environment of a software application and build a plurality of model application environments based on the obtained information. The system runs the software application using the current application environment and each of the model application environments. The system collects a plurality of performance metrics related to performance of the software application in the current application environment and each of the model application environments while running in the simulated environment. The system generates a recommendation report based on the performance metrics, wherein the recommendation report comprises a recommendation of a different technology product for at least one of the technology components used in the current application environment, wherein the different technology product is different from a current technology product used for the at least one technology component in the current application environment.

A computer-implemented method for detecting bottlenecks in microservice cloud systems is provided including identifying a plurality of nodes within one or more clusters associated with a plurality of containers, collecting thread profiles and network connectivity data by periodically dumping stacks of threads and identifying network connectivity status of one or more containers of the plurality of containers, classifying the stacks of threads based on a plurality of thread states, constructing a microservice dependency graph from the network connectivity data, aligning the plurality of nodes to bar graphs to depict an average number of working threads in a corresponding microservice, and generating, on a display, an illustration outlining the plurality of thread states, each of the plurality of thread states having a different representation.

A system is configured to obtain information relating to a current application environment and a plurality of model application environments of a software application. The system runs the software application using the current application environment and each of the model application environments. The system collects a plurality of performance metrics related to performance of the software application in the current application environment and each of the model application environments while running in the simulated environment. The system assigns a score to each performance metric and determines a model application environment that yielded a higher score for a performance metric as compared to the score of the performance metric in the current application environment. The system recommends at least one technology product used for a corresponding technology component associated with the performance metric in the determined model application environment.