Methods and systems for detecting and responding to an anomaly include determining a first system-level performance prediction using system-level statistics. A second system-level performance prediction is determined using system-level statistics and service-level statistics. The first prediction to the second prediction are compared to identify a discrepancy. It is determined that a service corresponding to the service-level statistics is a cause of a detected failure in a distributed computing system. An action directed to the service is performed responsive to the detected failure.

In one embodiment, an apparatus includes: a first trace source to generate a plurality of first trace messages and a first local platform description identifier to identify the first trace source; a second trace source to generate a plurality of second trace messages and a second local platform description identifier to identify the second trace source; and a trace aggregator coupled to the first and the second trace sources, the trace aggregator to generate a global platform description identifier for the apparatus and output a trace stream including the global platform destination identifier, the first and second local platform description identifiers, the plurality of first trace messages and the plurality of second trace messages. Other embodiments are described and claimed.

A method and system for verifying state monitor reliability in hyper-converged infrastructure (HCI) appliances. Specifically, the method and system disclosed herein entail using a supervised machine learning model—i.e., a classification decision tree—to accurately distinguish whether conflicting event notifications, logged across multiple state monitors tracking state on an HCI appliance, are directed to a real event or a non-real event. The classification decision tree, generated based at least on information gains calculated for the multiple state monitors, may reflect which state monitor(s) is/are more reliable in accurately classifying the conflicting event notifications.

Improved mechanisms and techniques for recording and aggregating trace information from multiple computing modules of a storage system may be provided. On a storage system having multiple computing modules, where each computing module has multiple processing cores, processing cores may record trace information for I/O operations in dedicated local memory—i.e., memory in the same computing module as the processing core that is dedicated to the computing module. One of the processing cores may be configured to aggregate trace information from across multiple computing modules into its dedicated local memory by accessing trace information from the dedicated local memories of the other computing modules in addition to its own. The aggregated information in one dedicated local memory then may be analyzed for functionality and/or performance and additional action taken based on the analysis.

A method includes receiving, from one or more sensors associated with manufacturing equipment, current trace data associated with producing, by the manufacturing equipment, a plurality of products. The method further includes performing signal processing to break down the current trace data into a plurality of sets of current component data mapped to corresponding component identifiers. The method further includes providing the plurality of sets of current component data and the corresponding component identifiers as input to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of predictive data and causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.

Requests from file system services of a storage system are registered. Each file system service, when executed, utilizes one or more resources of the storage system. Each request includes information describing resource requirements required by a respective file system service. Resource utilization data of the resources are collected over a period of time. The resource utilization data includes an identification of a resource, a timestamp, and a measurement indicating a utilization level of the resource corresponding to the timestamp. A machine learning model is trained to predict utilization patterns of the resources. Execution of the file system services are scheduled based on the predicted utilization patterns. Monitoring is conducted during the execution of the file system services. Based on the monitoring a determination is made as to whether the machine learning model should be retrained.

Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.

A trace network for debugging integrated circuits is disclosed. At least one functional network includes a plurality of components interconnected by a number of network switches, implemented on at least one integrated circuit. A trace network is also implemented on the at least one integrated circuit, and includes a plurality of trace circuits configured to generate trace data based on transactions between ones of the plurality of components. The plurality of trace circuits are coupled to one another by a plurality of trace network switches. The trace circuits are configured to convey the generated trace data to an interface, via the trace network, without using the at least one functional network.

Various embodiments of the present invention address technical challenges related to software testing and make substantial technical improvements to improving the computational efficiency and operational reliability of test automation platforms, as well as to the operational reliability of software applications that are tested using the software application platforms. Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing efficient and techniques for visual software test management using captured test case data entities, annotation-based test case data entities, and dynamic test case data entity cloning.

A web server operating in a container has resource and network limits applied to add an extra layer of security to the web server. If a monitor detects that the container's resource usage is approaching one or more of these limits, which may be indicative of a DDoS attack, (step 210) or identifies traffic sources exhibiting suspicious behaviour, such as frequently repeated requests from the same address, or from a related set of addresses, a restrictor function caps the resources allowed by the original Webserver container to allow it to recover from buffer overflow and protect servers running in other containers from overwhelming any shared resources. A duplicator function starts up replica containers with the same resource limits to take overflow traffic, and a load balancing function then directs incoming traffic to these overflow containers etc. Traffic from suspicious sources is directed by the load balancer to one or more specially-configured attack-assessment container(s) where a ‘dummy’ web server operates. The behaviour of these sources is analysed by a behaviour monitoring function over some time to determine if they are legitimate or malicious, which can control a firewall to block addresses identified as generating malicious traffic.