A system and method in accordance with examples may include systems and methods for detecting failure of microservice applications in communication with an orchestration layer of a microservice-architecture. The system may include memory and an orchestration layer including one or more processors coupled to the memory. The one or more processors may be configured to connect the orchestration layer to a plurality of microservice applications that are each associated with a respective dataset. The one or more processors may be configured to validate, responsive to the connection of each of the microservice applications, the microservice applications by performing a first test and a second test. The one or more processors may be configured to deploy, responsive to the validation of the microservice applications, the microservice applications to execute a plurality of workflow actions.

A motor control device avoids the difficulties associated with safely controlling a motor using a second calculation unit when a first calculation unit malfunctions. A motor control device monitors a malfunction of a first microcomputer and a malfunction of a first CPU of the first microcomputer and performs a determination based on a result of a second external communication functional unit. When the determination is normal, the process proceeds determine that the first CPU is normal and a second CPU malfunctions. Therefore, it is possible to continue processing using the first CPU in this case.

Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.

Embodiments herein may present a multi-tile processor including a plurality of processor tiles, and a plurality of interconnects selectively coupling the plurality of processor tiles to each other. A first processor tile may include a memory to store a bulletin board to hold a message, an execution unit, and an encapsulated software module. The encapsulated software module may select a second processor tile coupled with the first processor tile by an interconnect to be a part of a signal pathway. The second processor tile may be selected based on a selection criterion of the signal pathway and the message held in the bulletin board. The encapsulated software module may post and read a message at the bulletin board stored in the memory, or read a message from a bulletin board stored in a memory of the second processor tile. Other embodiments may be described and/or claimed.

A system for connecting user action flows is disclosed. The system determines when a first object is created on a first thread in response to a first user action. Additionally, the system stores a first relationship between the first thread and the first object based on the determination of when the first object is created. Moreover, the system determines when the first object is running on a second thread that differs from the first thread, and stores a second relationship between the second thread and the first object based on the determination of when the first object is running.

In an approach to cleanup of unpredictable test results, one or more computer processors generate a data area associated with a first test instruction in a test stream. The one or more computer processors determine whether the generated data area overlaps with an unpredictable data area. In response to determining the generated data area overlaps with an unpredictable data area, the one or more computer processors determine a second test instruction associated with the overlapped unpredictable data area, where the second test instruction precedes the first test instruction in the test stream. The one or more computer processors select a location in the test stream between the first test instruction and the second test instruction. The one or more computer processors insert one or more pre-requisite instructions in the selected location, where the one or more pre-requisite instructions load the overlapped unpredictable data area with pre-defined data.

In an approach to cleanup of unpredictable test results, one or more computer processors generate a data area associated with a first test instruction in a test stream. The one or more computer processors determine whether the generated data area overlaps with an unpredictable data area. In response to determining the generated data area overlaps with an unpredictable data area, the one or more computer processors determine a second test instruction associated with the overlapped unpredictable data area, where the second test instruction precedes the first test instruction in the test stream. The one or more computer processors select a location in the test stream between the first test instruction and the second test instruction. The one or more computer processors insert one or more pre-requisite instructions in the selected location, where the one or more pre-requisite instructions load the overlapped unpredictable data area with pre-defined data.

A data structure and a mechanism to manage storage of objects is disclosed. The data structure can be used to manage storage of objects on any storage device, whether in memory or some other storage device. Given an object ID (OID) for an object, the system can identify a tuple that includes a device ID and an address. The device ID specifies the device storing the object, and the address specifies the address on the device where the object is stored. The application can then access the object using the device ID and the address.

Embodiments described herein are related to cloning a volume in a file system. In some embodiments, a directory hard link is used to generate a clone of the root node of the volume. In certain embodiments, upon determining that a file or directory of the clone which comprises a hard link to an index node has been modified, a new object directory is generated beneath a root node of the volume. The index node may be added to the new object directory and one or more files and directories in the volume which link to the index node may be updated to contain symbolic links to the index node in the new object directory. In certain embodiments, a copy-on-write operation is performed in order to copy the file or directory and the new object directory to the clone.

Embodiments described herein are related to cloning a volume in a file system. In some embodiments, for each index node representing a file or directory in the volume, the index node is updated to include a back pointer to each file or directory which points to the index node in the volume. In some embodiments, a copy-on-write operation is performed in order to generate a clone of the volume's root node. In certain embodiments, upon determining that a file or directory of the clone has been modified, a new index node is generated representing the file or directory by copying an index node representing a corresponding file or directory of the volume. In some embodiments, each file or directory which should point to the new index node in the clone is identified based on one or more back pointers and updated to point to the new index node.