Abstract: Methods and systems for selecting and executing optimal execution strategies for processes, wherein an optimal execution strategy may be selected from a plurality of execution strategies for example in a look-up table, based on specific process values.

Abstract: A Multi-site Integrated Session-oriented Transaction (MIST) computing system includes a MIST mapper configured to process a compiled distributed compute session request to determine, for each of at least one remote sets of data, whether to process the remote set of data with one of one or more field nodes or with a cloud node based on a cost of transporting the remote set of data from the proximate field node to the cloud node. In response to determining to process the remote set of data with the cloud node, the MIST mapper is configured to output a data retrieval request to the proximate field node. In response to determining to process the remote set of data with the field node, the MIST mapper is configured to output the computation request to the proximate field node.

Abstract: Techniques for tracking a lock involve creating an event comprising object information, and extracting the object information in the event through a thread in a thread pool. Such techniques further involve recording lock information comprising the object information according to a determination that the thread executes a lock operation. Such a lock tracking technique for a threading model can record lock information of an object when a thread performs locking and unlocking, and realize tracking of a lock operation and generating a lock analysis report.

Abstract: A control plane system can be used to manage or generated components in a shared computing resource environment. To generate a modified components, the control plane system can receive receiving configurations of a component. The configurations can include software versions and/or parameters for the component. Using the configurations, the control plane system can generate an image of a modified component, and communicate the image to a master node in the shared computing resource environment. The master node can provides one or more instances of the modified component for use based on the received image.

Abstract: A method may include receiving an indication that a recalculation operation is to be completed for data stored in a data file; determining that a currently assigned number of threads for execution of the recalculation operation is lower than a target number of threads for the recalculation operation; requesting an additional thread for execution of the recalculation operation; beginning execution of the recalculation operation using the currently assigned number of threads; receiving an indication that the additional thread is available for execution of the recalculation operation; updating the currently assigned number of threads to include the additional thread; and continuing execution of the recalculation operation using the updated currently assigned number of threads.

Abstract: Provided is a method for scheduling resource-constrained project by IWO. The method establishes a resource-constrained project scheduling model first, converts the project scheduling issue in actual engineering into the combination and optimization in a mathematical model, then the optimization aims at minimizing the project total duration, while considering the immediate predecessor/successor constraints of the project activities and a variety of renewable resource constraints, to construct project scheduling model, lastly IWO is used to seek the solution for large-scale project scheduling. In the solution process, a right-shift decoding strategy was designed to rectify the ineligible solutions that occurred during the generation of weed seeds, ensure that all solutions are in compliance with the immediate predecessor/successor constraints of the project activities, and improve the efficiency of algorithm. Solution efficiency.

Abstract: A notification of an event associated with a computing instance provided to customers of a computing resource service provider is obtained. Log information associated with the computing instance is obtained from a log of operations of the computing instance, with the log information indicating events associated with the computing instance. Pattern matching on the log information is performed to produce a pattern matching result. The computing instance is determined to be impaired based at least in part on the pattern matching result including indications of impairment. A mitigation operation is performed to the computing instance to mitigate the impairment.

Abstract: Methods and systems for transforming data archives are provided. In one embodiment, a method is provided that includes receiving a data archive and selecting a first data item from among a plurality of data items contained within the data archive. A first data format of the first data item may be identified that specifies a first type of data stored in the first data item and a first data transformation may be selected that corresponds to the first data format. The first type of data may be transformed by the first data transformation to generate a first transformed data item. The first data item may be replaced with the first transformed data item in the data archive and an executable item contained within the data archive may be executed.

Abstract: A computing device and a method for operating the computing device are provided. The computing device includes a task classifying module and a task allocating and managing module. The task classifying module classifies a task scheduled to be processed by a multi-core processor into an expectable task or a normal task. The task allocating and managing module selects one core of the multi-core processor as a target core and allocates the task scheduled to be processed to the target core. In response to the task scheduled to be processed being classified as the normal task, the task allocating and managing module determines whether to allocate the normal task to the target core depending on whether the target core processes a previously allocated expectable task.

Abstract: A method of managing a stack includes detecting, by a stack manager of a processor, that a size of a frame to be allocated exceeds available space of a first stack. The first stack is used by a particular task executing at the processor. The method also includes designating a second stack for use by the particular task. The method further includes copying metadata associated with the first stack to the second stack. The metadata enables the stack manager to transition from the second stack to the first stack upon detection that the second stack is no longer in use by the particular task. The method also includes allocating the frame in the second stack.

Abstract: Testing methods and systems are provided for testing a resource manager of an application management system. The testing systems include a load orchestrator configured to obtain an artificial metric that is determined based on a utilization model (e.g., CPU usage, memory allocation, or disk usage, number of webserver sessions). The load orchestrator transmits the artificial metric to applications in a cluster of computing nodes. The applications transmit the artificial metric to the resource manager. In response, the resource manager generates control output for managing applications in the cluster based on the artificial metric (e.g., scaling, load balancing, application placement, failover of applications, or defragmenting data). The utilization model may include executable code for generating artificial metric values. The model may be received as a result of an API call. The load orchestrator may be instantiated in an orchestration framework or in each node of the cluster.

Abstract: Disclosed are various embodiments for separating creation of and executing of updated campaigns in multitenancy environments. For example, an organization can have a hierarchical structure such that a root organization includes multiple sub-organizations. A root organization administrator may create an update campaign for enforcing software policies on devices that are included in the sub-organizations. A sub-organization administrator can approve and define the execution schedule for when the devices included in the sub-organization can execute the update campaign.

Abstract: Technologies for power-aware scheduling include a computing device that receives network packets. The computing device classifies the network packets by priority level and then assigns each network packet to a performance group bin. The packets are assigned based on priority level and other performance criteria. The computing device schedules the network packets assigned to each performance group for processing by a processing engine such as a processor core. Network packets assigned to performance groups having a high priority level are scheduled for processing by processing engines with a high performance level. The computing device may select performance levels for processing engines based on processing workload of the network packets. The computing device may control the performance level of the processing engines, for example by controlling the frequency of processor cores. The processing workload may include packet encryption. Other embodiments are described and claimed.

Abstract: Quality of service is provided to prioritized VMs and applications, based on the varied quality of different shared computing resources. Each VM or application has an associated priority. A quality rating is dynamically assigned to each shared computing resource. Requests for shared computing resources made by specific VMs or applications are received. For each specific received request, the current priority of the requesting VM or application is identified. In response to each received request, a specific shared computing resource is assigned to the specific requesting VM or application. This assignment is made based on the current priority of the requesting VM or application and the current quality rating of the shared computing resource, thereby providing quality of service to the requesting VM or application corresponding to its current priority.

Abstract: A method for managing data includes obtaining, by a first data node, a notification, wherein the first data node is associated with a first power zone group (PZG), and in response to the notification: selecting a second data node, wherein the second data node is not associated with the first PZG, sending a data processing request to the second data node, obtaining a response based on the data processing request, wherein the response specifies a confirmation by the second data node to service the data processing request, storing a ledger entry in a ledger service that indicates the confirmation, and initiating a data transfer based on the data processing request, wherein the first data node is associated with the PZG based on a primary power source of the first data node.

Abstract: A parallel processing unit (PPU) can be divided into partitions. Each partition is configured to operate similarly to how the entire PPU operates. A given partition includes a subset of the computational and memory resources associated with the entire PPU. Software that executes on a CPU partitions the PPU for an admin user. A guest user is assigned to a partition and can perform processing tasks within that partition in isolation from any other guest users assigned to any other partitions. Because the PPU can be divided into isolated partitions, multiple CPU processes can efficiently utilize PPU resources.

Abstract: In a sequence of major computational steps or in an iterative computation, a stencil amplifier can increase the number of data elements accessed from one or more data structures in a single major step or iteration, thereby decreasing the total number of computations and/or communication operations in the overall sequence or the iterative computation. Stencil amplification, which can be optimized according to a specified parameter such as compile time, rune time, code size, etc., can improve the performance of a computing system executing the sequence or the iterative computation in terms of run time, memory load, energy consumption, etc. The stencil amplifier typically determines boundaries, to avoid erroneously accessing data elements not present in the one or more data structures.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented as a computational graph on a distributed computing network. A method includes: receiving data representing operations to be executed in order to perform a job on a plurality of hardware accelerators of a plurality of different accelerator types; generating, for the job and from at least the data representing the operations, features that represent a predicted performance for the job on hardware accelerators of the plurality of different accelerator types; generating, from the features, a respective predicted performance metric for the job for each of the plurality of different accelerator types according to a performance objective function; and providing, to a scheduling system, one or more recommendations for scheduling the job on one or more recommended types of hardware accelerators.

Abstract: Systems, apparatuses and methods may provide for technology that associates a key domain of a plurality of key domains with a customer boot image, receives the customer boot image from the customer, and verifies the integrity of the customer boot image that is to be securely installed at memory locations determined from an untrusted privileged entity (e.g., a virtual machine manager).

Abstract: The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.