Abstract: An electronic device is provided. An electronic device includes at least one antenna module, a first communication circuit configured to provide first communication via the at least one antenna module, a plurality of temperature sensor, at least one processor operationally connected to the first communication circuit and the plurality of temperature sensors, and a memory.

Abstract: Techniques for secure and efficient interfacing with a cloud computing service are described. In an embodiment, a cloud computing management service is programmed or configured to communicate with a cloud computing service. The cloud computing management service can be accessed by software engineers that are looking to deploy a software instance to a computing device of the cloud computing service. Thus, the cloud computing management service acts as an intermediary layer in front of the cloud computing service. In an embodiment, the cloud computing management service may store one or more frequently-used system parameters for deployment of software instances. The parameters conform to company's security protocols, compliance protocols, and/or other standards.

Abstract: Techniques described herein relate to a method for deploying workflows.

Abstract: Aspects disclosed in the detailed description include multi-level instruction scheduling in a processor. Related methods and systems are also disclosed. In one exemplary aspect, an apparatus is provided that comprises a scheduler circuit comprising a scheduling group circuit, a first selection circuit, and a second selection circuit. The scheduling group circuit comprising a plurality of groups of scheduling entries, each scheduling entry among the groups of scheduling entries each comprising an instruction portion and a ready portion, each group configured to have its scheduling entries written in-order. The scheduling group circuit is further configured to maintain group age information associated with each group of the plurality of groups. The first selection circuit is configured to select a first in-order ready entry from each group. The second selection circuit is configured to select the first in-order ready entry belonging to the oldest group based on the group age information for scheduling.

Abstract: A deep learning job scheduling method includes obtaining a job request of a deep learning job, determining a target job description file template from a plurality of pre-stored job description file templates based on the job request, determining an identifier of a target job basic image from identifiers of a plurality of pre-stored job basic images based on the job request, generating a target job description file based on the target job description file template and the identifier of the target job basic image, sending the target job description file to a container scheduler, and selecting the target job basic image from the pre-stored job base images based on the target job description file, and creating at least one container for executing the job request.

Abstract: The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.

Abstract: A task processing method, a processing apparatus, and a computer system are provided. Implementation of the method includes: generating, by a first processing apparatus, a plurality of tasks, and determining task description information of the plurality of tasks, where the task description information of the plurality of tasks is used to indicate a dependency relationship between the plurality of tasks; sending an instruction to a second processing apparatus, where the instruction includes the plurality of tasks and the task description information of the plurality of tasks; and receiving the instruction, and processing the plurality of tasks based on the dependency relationship between the plurality of tasks. The method can effectively reduce a waiting delay, fully exploit a computing capability of an acceleration chip, and improve task processing efficiency.

Abstract: Disclosed is an electronic device including at least one processor, and a memory operatively coupled to the at least one processor. The memory stores instructions configured to enable the at least one processor to identify, in response to running of an application, a plurality of tasks related to a running operation of the application, allocate virtual runtimes to the plurality of tasks when scheduling, adjust the virtual runtime of at least one task to be run with priority among the plurality of tasks to be a minimum value, arrange the at least one task with the adjusted virtual runtime, and run the at least one task with priority according to an arrangement order.

Abstract: A system and method are disclosed for orchestrating the execution of computing tasks. An orchestration engine can receive task requests over a network from a plurality of process engines. The process engines may correspond to respective edge or field devices that are remotely located as compared to the orchestration engine. Each task request may indicate at least one task requirement for executing a respective computing task. A plurality of computing instances that have available computing resources can be selected from a set of computing instances. A predicted runtime can be generated for each of the computing tasks. In an example, based on the predicted runtimes, task requirements, available computing resources, and associated network conditions, a schedule and allocation scheme are determined by the orchestration engine.

Abstract: Example methods are provided to identify unused memory regions in pages that are allocated for storing executable code. One or more of the unused memory regions are usable as a secure location to store confidential information shared between a hypervisor on the host and a guest (such as a guest virtual computing instance) that runs on the host. The one or more unused memory regions may also be used to store executable code (such as valid executable code of antivirus software or other security program) that has been prevented/delayed in its execution by malicious code that has occupied the pages, thereby providing the executable code with sufficient memory resources to enable the executable code to at least partially complete execution.

Abstract: The present disclosure improves usability and stability of a control system while suppressing resource consumption. A control controller according to the present disclosure divides, using partitioning, function groups for which control is ongoing and function groups to be updated, and updates, in accordance with conditions, only the function groups to be updated, thereby updating the required control functions while continuing appropriate control processes.

Abstract: A method of logging thread parallelism data include executing a plurality of threads at a multicore processor associated with an operating system to perform symmetrical multiprocessing. The method also includes tracking, at a logging subsystem of the operating system, an accumulated runtime associated with each thread combination of the plurality of threads during execution of the plurality of threads. The accumulated runtime of a particular thread combination increases while the particular thread combination is running on the multicore processor in parallel. The method also include generating, at the logging subsystem, logging data indicating the accumulated runtime for each thread combination. The method further includes outputting the logging data. The logging data is usable to increase thread parallelism at the multicore processor.

Abstract: A task scheduling method includes: when a task requirement is obtained, splitting the task requirement to obtain the plurality of subtasks having a constraint relationship; performing execution condition detection on non-candidate subtasks, determining a non-candidate subtask that satisfies an execution condition as a candidate subtask, and putting the candidate subtask into a task queue; performing state detection on a server network composed of edge servers to obtain server state information and communication information; inputting the server state information, the communication information, and queue information corresponding to the task queue into an action value evaluation model to obtain the plurality of evaluated values respectively corresponding to the plurality of scheduling actions; and determining a target scheduling action from the plurality of scheduling actions by using the evaluated values, and scheduling the candidate subtask in the task queue on the basis of the target scheduling action.

Abstract: Systems and methods for intent-based orchestration of independent automation are described.

Abstract: A computer system configured to autonomously provision at least a portion of a desktop as a service (DaaS) system is provided. The computer system includes a memory, a network interface, and a processor configured to receive, via the network interface, a request to add a catalog of virtual desktops to an account associated with a tenant of the computing service, and transmit, in response to reception of the request, a plurality of requests to the computing service. The plurality of requests comprise at least one request to create a first virtual network associated with the tenant within the computing service, at least one request to connect the first virtual network to a second virtual network within the computing service, and at least one request to create the catalog within the computing service.

Abstract: In accordance with input received via a programmatic interface, a level of isolation at which a software container is to be executed is determined. Based on the level of isolation, a category of virtual machines of a virtualized computing service is selected for executing the software containers. The selected category differs from other categories in at least the number of devices emulated for virtual machines of the categories. The software container is run within a virtual machine of the selected category.

Abstract: In some examples, a data management and storage (DMS) system comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and an IO request scheduler comprising at least one processor configured to perform operations in a method of scheduling IO requests. Example operations comprise implementing a kernel scheduler to schedule a flow of IO requests in the DMS system, and providing an adjustment layer to adjust the kernel scheduler based on an IO request prioritization. A flow of IO requests is identified and some examples implement an IO request prioritization based on the adjustments made by the adjustment layer.

Abstract: This disclosure is directed to systems and methods for determining target allocation parameters for initiating targeted communications in complex computing networks, which may be associated with the allocation of allocatables in execution events over a period of time. The systems and methods may include receiving a desired allocation; determining a first available allocation at a first time; generating allocation information for a second period comprising the first time; determining a second available allocation at a second time; determining a remaining available allocation, based on the allocation information and the second available allocation; and determining one or more target allocation parameters for initiating a targeted communication to a computing device after the second time.

Abstract: A method for instantiating and transparently migrating executing containerized processes includes receiving, by a container engine executing on a first machine, an instruction to instantiate a container image on the first machine. The container engine transmits, to a modified container runtime process, executing on the first machine, the instruction to instantiate the container image on the first machine. The modified container runtime process generates, on the first machine, a shim process representing the instantiated container image. The shim process forwards the instruction to an agent executing on a second machine, via a proxy connected to the agent via a network connection. The agent directs instantiation of the container image as a containerized process. A scheduler component executing on the first machine determines to migrate the containerized process to a third machine.

Abstract: Embodiments disclosed include data center infrastructure management (DCIM) systems and methods configured to collect data center compute systems, power systems, and facility systems data, trigger an action or actions based on a diagnosed or predicted condition according to the collected data, and thereby control via a compute, power, and facilities module, the compute systems, power systems and facility systems in the data center. According to an embodiment, the control via the compute, power, and facilities module comprises calibrating the compute, power, and facility systems based on an estimated compute requirement, and an associated power, cooling, and network data resource requirement. The estimated compute requirement comprises estimating compute density per real-time power wattage, and storage density per real-time power wattage.