Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

System and methods are described for performance analysis of a cloud computing environment. During a capture mode during a production use of a database system, the system captures user requests to access the cloud computing environment, asynchronously writes the user requests in one or more batches to a first database; and moves the user requests in one or more batches to a second database. During a replay mode during a performance analysis use of the cloud computing environment, the system reads the user requests from the second database and replays the user requests against the cloud computing environment in a first run, collects performance measurements for the first run, makes one or more changes to the cloud computing environment, reads the user requests from the second database and replays the user requests against the cloud computing environment in a second run, collects performance measurements for the second run, and compares performance measurements from the first run to performance measurements from the second run.

The present disclosure relates to computer-implemented methods, software, and systems for managing data replication. A request associated with storing content of a file is received at a storage service provided by in a multiple availability zone cloud platform. A lock request is sent to an in-memory data grid at a first instance of the storage service to lock the file for accessing. An input stream of the file is received at the persistence interface to be read iteratively in portions. A read portion of the file is iteratively stored in a first file system storage associated with instances of the storage service at a first availability zone. The portions of the file are provided iteratively to a replication executor at the first instance of the storage service to request replication of the content of the file into a second file storage of a second availability zone of the cloud platform.

A computer system receives, in a first messaging conversation by a first messaging application of a plurality of applications, information identifying a first shared content item. In response to receiving the information identifying the first shared content item, in accordance with a determination that the first shared content item is of a first type, the computer system automatically makes the first shared content item available within a first application of the plurality of applications, the first application is associated with content of the first type. In accordance with a determination that the first shared content item is of a second type, the computer system automatically makes the first shared content item available within a second application of the plurality of applications, wherein the second application is associated with content of the second type.

Self-tuning clusters for resilient microservices, including: receiving, by a services orchestrator within a cloud-computing environment and from a plurality of cloud computing instances, respective latency measurements corresponding to respective control plane operations directed to a shared resource of the plurality of cloud computing instances; determining, based on a current timeout value and on the respective latency measurements from the plurality of cloud computing instances, an updated timeout value for the shared resource; and providing, to each of the plurality of cloud computing instances, the updated timeout value for the shared resource.

A processing device is provided which comprises a plurality of compute units configured to process data, a plurality of arithmetic logic units, instantiated separate from the plurality of compute units, and configured to store the data at the arithmetic logic units and perform calculations using the data and an interconnect network, connecting the arithmetic logic units and configured to provide the arithmetic logic units with shared access to the data for communication between the arithmetic logic units. The interconnect network is also configured to provide the compute units with shared access to the data for communication between the compute units.

A control system for a valve or other flow control device can include a processor device. The control system can further include a memory in communication with the processor device. The memory may have a fixed maximum capacity. The control system can further include one or more ports to receive signals corresponding to events for the valve or other flow control device. The processor device can be configured to execute operations that include: over a time interval, counting a quantity of events of a first type, corresponding to the signals received at the one or more ports, and after the time interval, storing in the memory a record of the first type of event over the time interval, based on the counted quantity.

Methods, systems, and devices for pipeline fusion of a plurality of kernels. In some implementations, a first batch of a first kernel is executed on a first processing device to generate a first output of the first kernel based on an input. A first batch of a second kernel is executed on a second processing device to generate a first output of the second kernel based on the first output of the first kernel. A second batch of the first kernel is executed on the first processing device to generate a second output of the first kernel based on the input. The execution of the second batch of the first kernel overlaps at least partially in time with executing the first batch of the second kernel.

Systems, apparatuses, and methods for implementing continuation analysis tasks (CATs) are disclosed. In one embodiment, a system implements hardware acceleration of CATs to manage the dependencies and scheduling of an application composed of multiple tasks. In one embodiment, a continuation packet is referenced directly by a first task. When the first task completes, the first task enqueues a continuation packet on a first queue. The first task can specify on which queue to place the continuation packet. The agent responsible for the first queue dequeues and executes the continuation packet which invokes an analysis phase which is performed prior to determining which dependent tasks to enqueue. If it is determined during the analysis phase that a second task is now ready to be launched, the second task is enqueued on one of the queues. Then, an agent responsible for this queue dequeues and executes the second task.

Methods and systems are provided for processing a stream of incoming messages sent from a specific input message source and validating each incoming message of that stream before sending them to a specific target system.