Apparatus and methods for performing a Million Instructions per Second (MIPS) analysis for a data stack of a user equipment (UE) are disclosed. The method includes (i) receiving an input for a Monte Carlo simulation, the input including a requirement for one or more use cases, a processor specification, and a user-specified function; (ii) determining a traffic model, a number of packets to be run for each use case, and a seed value for the Monte Carlo simulation; (iii) performing the Monte Carlo simulation based on the input and the traffic model to generate a simulation result; and (iv) determining a recommended configuration of processor cores for the data stack based on the simulation result.

A simulator extracts sensor data from multiple systems. The sensor data includes measurements taken by sensors disposed at the multiple systems. The simulator standardizes the sensor data into a common format and classifies the sensor data according to a performance metric. A model of a target system for the performance metric is generated based on the standardized sensor data. The simulator can simulate the impact on the performance metric for a target system based on a simulated change to the multiple systems. The simulator can generate a network interface including a tool that enables end users to interact with the simulation and to determine procedures for mitigating the impact.

Artificial intelligence (AI) model monitoring and ranking includes obtaining metric values indicative of performance of AI model deployments, the metric values including respective metric values measured across metrics, determining violation statuses of the metrics for each of the AI model deployments, the violation statuses indicating, for each AI model deployment, which of the metrics are violated by the AI model deployment as reflected by respective metric values for that AI model deployment, ranking the AI model deployments against each other according to a ranking model and based on the determined violation statuses for each of the AI model deployments, and providing a rank of at least some of the AI model deployments to a user.

A computer system that detects an abnormality based on time series data, including: an abnormality diagnosis unit that diagnoses an abnormality of the time series data from a machine learning model created based on learning data; a model degradation detection unit that detects degradation in the machine learning model; a learning curve estimation unit that estimates a learning curve and predicts a number of errors per unit time; a model switch cost calculation unit that calculates a number of errors per unit time of a model in operation, a number of errors per unit time of a switch candidate model, a first total cost and a second total cost; and a model switch time prediction unit that compares the first total cost with the second total cost to calculate switch time of a machine learning model.

A method and apparatus for testing a map service are provided. The method may include: determining a to-be-screened service request based on a service request of an electronic map recorded in advance at a preset sampling frequency; screening the to-be-screened service request by using a static rule, to obtain a first valid service request set; screening the to-be-screened service request by using a dynamic test step, to obtain a second valid service request set; and testing a service of the electronic map based on the first valid service request set and the second valid service request set.

Computer-implemented methods using machine learning are provided for generating an estimated cache performance of a cache configuration. A neural network is trained using, as inputs, a set of memory access parameters generated from a non-cycle-accurate simulation of a data processing system comprising the cache configuration and a cache configuration value, and using, as outputs, cache performance values generated by a cycle-accurate simulation of the data processing system comprising the cache configuration. The trained neural network is then provided with sets of memory access parameters generated from a non-cycle-accurate simulation of a proposed data processing system and a selected cache configuration and generates estimated cache performance values for that selected cache configuration.

A shift-left analysis system receives information regarding an application implemented by one or more microservices. The system determines a microservice performance metric based on a performance prediction model for each microservice of the application. The system outputs an application performance metric for the application based on the microservice performance metrics determined for the one or more microservices of application.

In one or more embodiments, a system and/or a method may implement: receiving data representing different performance behavior metrics that are associated with software instances that are respectively associated with consumer computers and that specify values of performance factors of the software instances as the consumer computers interoperate with the software instances; determining data throughput values that represent processing throughput of the software instances; determining relative capacity values of the software instances; adapting each of prediction models to the relative capacity values; determining correlation coefficients from the prediction models and the multiple data throughput values; executing a prediction model of the prediction models associated with a particular correlation coefficient closest to result in outputting threshold values associated with the performance factors; and transmitting the threshold values to a particular consumer computer among the consumer computers as part of a change recommendation message having a hyperlink.

Methods, systems, and computer-readable media are disclosed herein for a system and framework that tests end-user applications for failures, data validation, and performance indicators. In aspects, multiple use-modeling programs that mimic user interactions are used to concurrently run unique instances of the application in real-time to simulate a real-world scenario, at scale and with a full load. Whether data, operations, and/or functions of the end-user application fail or are successful is automatically documented in real-time, while performance is concurrently measured.

A system and method for representing events that occur in a real world deployment is described. A real-world workload including multiple events is identified. Multiple characteristics of the real-world workload are converted into multiple endpoint simulator workloads. Multiple gateway hardware characteristics are converted into a modeling elements for simulated Internet of things (IoT) networks. Further, a simulation is performed for each of the endpoint simulator workloads on each of the simulated IoT networks. Also, statistics are collected about the performance of the simulated IoT networks for the endpoint simulator workloads.