Provided herein are systems and methods for multi-event correlation. Receiving a stream of events, each leaf rule engine may detect a plurality of events from the stream that matches a characteristic for the leaf rule engine. Each leaf rule engine may identify, from the plurality of events and within a time window, a group of events that satisfies a condition for the respective leaf rule engine. A root conditions engine may receive a stream of leaf events corresponding to the group of events identified by each leaf rule engine. The root conditions engine may identify, from the received stream of leaf events and within a root time window, a collection of events that satisfies a condition for the root conditions engine. A trigger may execute an action according to the collection of events identified within the root time window.

A program preparation system includes: a display unit displaying a first task input section to which a content of a first task to be executed by a target device is inputted and a second task input section to which a content of a second task to be executed by the target device is inputted, the second task being different from the first task; an intermediate code generation unit generating an intermediate code, using information inputted to the first task input section and information inputted to the second task input section; and a program conversion unit converting the intermediate code into a multitasking program causing the target device to execute the first task and the second task.

Embodiments relate to a system, program product, and method for leveraging thermal profiles of processing tasks to dynamically schedule execution of the processing tasks. Thermal profiles of the processing tasks are generated, where the thermal profiles include core hardware and core processing measurements and predictions of thermal performance based on the measurements. The execution of the processing tasks are scheduled in processing devices to mitigate a potential for reducing a margin to a hardware thermal limit.

Provided herein are systems and methods for multi-event correlation. Receiving a stream of events, each leaf rule engine may detect a plurality of events from the stream that matches a characteristic for the leaf rule engine. Each leaf rule engine may identify, from the plurality of events and within a time window, a group of events that satisfies a condition for the respective leaf rule engine. A root conditions engine may receive a stream of leaf events corresponding to the group of events identified by each leaf rule engine. The root conditions engine may identify, from the received stream of leaf events and within a root time window, a collection of events that satisfies a condition for the root conditions engine. A trigger may execute an action according to the collection of events identified within the root time window.

Techniques are provided for detection of resource bottlenecks in computing devices using artificial intelligence and causal graphs. A particular resource bottleneck can be identified as a cause of a current device issue based on a dynamic evaluation, by an anomaly detection module, of performance metrics of a computing device. Once a particular resource bottleneck has been identified as anomalous, one or more corresponding adjustments to configuration settings for the computing device can be identified to mitigate the current device issue using a causal graph that represents the dependencies among (i) various device issue types for a computing device, (ii) performance metrics of the computing device to evaluate for each device issue type, and (iii) one or more resources that may be a cause of a given device issue type. The corresponding adjustments to the computing device to improve the performance of the computing device can be automatically identified based on the resource determined to be the cause of the given device issue type.

A system and method for multi-region deployment of application jobs in a federated cloud computing infrastructure. A job is received for execution in two or more regions of the federated cloud computing infrastructure, each of the two or more regions comprising a collection of servers joined in a raft group for separate, regional execution of the job generating a copy of the job for each of the two or more regions. The job is then deployed to the two or more regions, the workload orchestrator deploying the job according to a deployment plan. A state indication is received from each of the two or more regions, the state indication representing a state of completion of the job by each respective region of the multi-cloud computing infrastructure.

An information processing apparatus includes a processor, a memory, and a storage device. The processor includes a plurality of sub-processors. The memory stores data of part of pages included in an address space allocated to processes executable in parallel using the plurality of sub-processors. The storage device retreats data of pages that are not stored in the memory. The processor acquires a working set size for each of the processes. The working set size indicates an amount of pages used for a unit time. The processor selects part of the processes when a sum of working set sizes of the processes exceeds a predetermined threshold value. The processor stops the selected processes for a predetermined time. The processor controls data of pages corresponding to the processes being stopped to be retreated from the memory to the storage device.

A method is provided comprising: executing a first set of files, and collecting a first set of performance data; updating the first set of files to produce a second set of files; executing the second set of files and collecting a second set of performance data; identifying a first subset of the first set of performance data; identifying a second subset of the first set of performance data; calculating a score based on the first subset and the second subset, the score indicating a difference in resource consumption between one or more first thread instances that are instantiated using the first set of files and one or more second thread instances that are instantiated using the second set of files; and generating and outputting a debugging message based on the score, wherein the first thread instances and the second thread instances have the same entry function and the same opcode.

According to aspects of the disclosure, a method is provided comprising: executing a set of threads in a storage system, the set of threads including at least a first thread; executing a plurality of performance counters of the storage system, the plurality of performance counters including at least: (i) a first performance counter that is executed when an operating state of the first thread is changed in response to the first thread accessing a synchronization object, and (ii) a second performance counter that is executed when a marker inserted in the first thread is executed; generating one or more performance data containers associated the first thread based on performance data associated with the first thread; and generating a directed graph based on the performance data containers.

A method is disclosed including: obtaining one or more values of a system metric, the system metric being associated with a hardware resource of a computing device; detecting whether the system metric is approaching a threshold, the threshold being associated with a key performance indicator (KPI) of the computing device, the detecting being performed based on the obtained values of the system metric; calculating a predicted value of the system metric in response to detecting that the system metric is approaching the threshold, the predicted value of the system metric being calculated by using a linear predictor that is trained using unevenly-sampled training data; detecting whether the predicted value of the system metric exceeds the threshold; and reconfiguring the computing device to prevent the system metric from reaching the predicted value in response to detecting that the predicted value exceeds the threshold.