A system for managing the operation of different components within a cloud system to accomplish various tasks, including the implementation of build features within the cloud system to achieve specific operational goals. The system may include a data defined infrastructure (DDI) tool installed within a data defined infrastructure (DDI) to manage certain features or tasks within the cloud system. The DDI may include an environment configuration database (ECDB), an orchestration engine, an automation engine, and/or other hardware and software components within the cloud system, such that the DDI tool installed on the DDI infrastructure may control operation of the ECDB, the orchestration engine, the automation engine, or other hardware and software components within the cloud system based on a set of data that fully describes the operational goal.

A method of test-assisted application programming interface (API) learning includes generating a machine-readable API specification (API spec). The API spec is based on application of machine learning and regular expression processes to an API documentation. The method includes comparing the API spec to other API specifications. Based on the comparison, the method includes identifying a set of similar API specifications. The method includes generating API test inputs based on the set of similar API specifications and the API spec. The method includes calling a native API system using a first API test input of the API test inputs. The method includes receiving a response indicative of whether the first API test input successfully interacts with the native API system. Based on the response, the method includes generating a feedback indicative of an alteration to the API test inputs or to the machine learning or the regular expression processes.

It is disclosed a method and an apparatus (80) of providing a function as a service, faas, deployment of an application. A deployment unit is generated (30, 44, 508) per group of application blocks, where said deployment unit comprises said group of application blocks, and an implementation of function invocation for functions being accessed by groups of application blocks. Function invocations of the group of application blocks are constrained or bound (604, 610, 612) to libraries of supporting implementations. Deployment units are provided (32, 48, 510) together with the element invocations attached to said libraries, to a lifecycle manager of a faas platform, whereby the faas platform implements the faas deployment of said application the performance targets of which, being related to the groups of application blocks. This disclosure enables a developer to adjust the performance of an application without having to change the logic of application implementations.

An apparatus is configured to be installed on a terminal block to make an electrical connection to at least one I/O loop. The apparatus includes a terminal section having at least one pair of electrical terminals. The electrical terminals are arranged to be connected to the terminal block and to the I/O loop. The apparatus further includes an electronic section electrically connected to the terminal section adapted to communicate with the I/O loop through the terminal section.

A processing system that includes a shared data fabric resets a first client processor while operating a second client processor. The first client processor is instructed to stop making requests to one or more devices of the shared data fabric. Status communications are blocked between the first client processor and a memory controller, the second client processor, or both, such that the first client processor enters a temporary offline state. The first client processor is indicated as being non-coherent. Accordingly, when the processor is reset some errors and efficiency losses due messages sent during or prior to the reset are prevented.

An information processing device connectable to a plurality of storage devices includes a power source circuit configured to supply power from a backup power source to each of the plurality of storage devices in response to a power loss event, and a processor. The processor is configured to transmit, to each of the storage devices, a first instruction to save user data that have been transmitted to the storage device and have not been written in a non-volatile manner, in response to the power loss event, and transmit, to at least one of the storage devices, a second instruction to save updated address translation information that corresponds to the user data and has not been reflected in an address translation table, upon receiving a response indicating completion of saving the user data from each of the storage devices.

The various embodiments of the present invention disclose a method for reducing interrupt latency in embedded systems. According to at least one example embodiment of the inventive concepts, the method for reducing interrupt latency in embedded systems, the method comprises steps of toggling, by a processor, from a supervisor (SVC) mode to an interrupt request (IRQ) mode on receiving an interrupt, identifying, by the processor, a Task Control Block (TCB) of a preempted task on receiving the interrupt, enabling, by the processor, the IRQ stack as a pseudo preempted task context table, and storing the preempted task context information in the IRQ stack, wherein a register set is stored in IRQ stack before processing the received interrupt.

Techniques are disclosed for reducing or eliminating loop overhead caused by function calls in processors that form part of a pipeline architecture. The processors in the pipeline process data blocks in an iterative fashion, with each processor in the pipeline completing one of several iterations associated with a processing loop for a commonly-executed function. The described techniques leverage the use of message passing for pipelined processors to enable an upstream processor to signal to a downstream processor when processing has been completed, and thus a data block is ready for further processing in accordance with the next loop processing iteration. The described techniques facilitate a zero loop overhead architecture, enable continuous data block processing, and allow the processing pipeline to function indefinitely within the main body of the processing loop associated with the commonly-executed function where efficiency is greatest.

A method for performing logging of modifications of a database includes, for each backend process of a plurality of backend processes simultaneously, writing a respective log entry to a write-ahead log buffer, submitting a respective commit request requesting the respective log entry be committed to a write-ahead log, and sleeping the respective backend process. The method also includes writing, using a dedicated writing process and direct asynchronous input/output, one or more of the respective log entries in the write-ahead log buffer to the write-ahead log. The dedicated writing process is different from each respective backend process of the plurality of backend processes. The method also includes updating a log sequence number pointer based on the respective log sequence numbers of the one or more of the respective log entries and waking, based on the log sequence number pointer, one or more of the respective backend processes.

A system for managing the operation of different components within a cloud system to accomplish various tasks, including the implementation of build features within the cloud system to achieve specific operational goals. The system may include a data defined infrastructure (DDI) tool installed within a data defined infrastructure (DDI) to manage certain features or tasks within the cloud system. The DDI may include an environment configuration database (ECDB), an orchestration engine, an automation engine, and/or other hardware and software components within the cloud system, such that the DDI tool installed on the DDI infrastructure may control operation of the ECDB, the orchestration engine, the automation engine, or other hardware and software components within the cloud system based on a set of data that fully describes the operational goal