The management computer has a memory which stores management information and management programs, and a CPU which refers to the management information and executes the management programs; the management information includes storage management information for allowing determination as to whether the plurality of storage resources can be paired in a redundant configuration, and couplable configuration management information for determining whether the plurality of storage resources and the plurality of server resources can be connected to each other; and when the CPU deploys a virtual machine, the CPU first determines, by reference to the storage management information, storage resources to be paired in a redundant configuration, then selects, by reference to the couplable configuration management information, server resources each of which can be connected to a respective one of the storage resources that are to be paired in a redundant configuration, and pairs the selected server resources in the redundant configuration.

Embodiments described herein are generally directed to intelligent management of microservices failover. In an example, responsive to an uncorrectable hardware error associated with a processing resource of a platform on which a task of a service is being performed by a primary microservice, a failover trigger is received by a failover service. A secondary microservice is identified by the failover service that is operating in lockstep mode with the primary microservice. The secondary microservice is caused by the failover service to takeover performance of the task in non-lockstep mode based on failover metadata persisted by the primary microservice. The primary microservice is caused by the failover service to be taken offline.

Concepts and technologies are disclosed herein for providing a network virtualization policy management system. An event relating to a service can be detected. A first policy that defines allocation of hardware resources to host the virtual network functions can be obtained, as can a second policy that defines deployment of the virtual network functions to the hardware resources. The hardware resources can be allocated based upon the first policy and the virtual network functions can be deployed to the hardware resources based upon the second policy.

Inflight transactions having predictable pod failure in distributed computing environments are managed by integrating a transaction manager into pods having containers running applications in a distributed computing environment, wherein the transaction manager records a transaction log having data indicative of historical pod failure. A pod health check that is also integrated into the pods determines predictive pod failure scenarios from the data of historical pod failure in the transaction log. Pod health can be tracked using the pod health checker by matching the predictive pod failure scenarios to transaction calls. Calls may be sent to a load balancer for recovery of pod failure for transaction calling match the predictive pod failure scenarios. Pods can be configured recover for the predictive pod failure.

The exemplary embodiments are related to a device, a system, and a method for implementing a hardware mechanism that is configured to validate the performance of scheduling software utilized by a safety-critical system. The hardware device may receive an indication that a first frame of a frame schedule is in use. The hardware device may also monitor a time parameter corresponding to the first frame. The hardware device may also determine whether an indication that a second frame of the frame schedule is in use is received prior to the expiration of the time parameter. When the indication that the second frame of the frame scheduler is in use is not received prior to the expiration of time parameter, the hardware device may send a signal to an operating system of the safety-critical system indicating that an error in executing the frame scheduled has occurred.

A displaying equipment at least including an image controlling module, a primary system module, and a system controlling module is disclosed. The image controlling module continuously receives an input image from an image sensitive device after activates, and directly outputs the received input image. The system controlling module constantly monitors the primary system module after activates to determine whether the primary system module activates completely. The primary system module runs an operating system after being activated to process the input image and to generate a processed image. After the primary system module activates completely, the image controlling module outputs both the input image and the processed image simultaneously. When the primary system module is abnormal, the image controlling module restores to output the input image only.

In accordance with an embodiment, described herein is a system and method for use in a distributed computing environment, for automatically scaling a cluster based on metrics being monitored. A cluster that comprises a plurality of nodes or brokers and supports one or more colocated partitions across the nodes, can be associated with an exporter process and alert manager that monitors metrics associated with the cluster. Various metrics can be associated with user-configured alerts that trigger or otherwise indicate the cluster should be scaled. When a particular alert is raised, a callback handler associated with the cluster, for example an operator, can automatically bring up one or more new nodes, that are added to the cluster, and then reassign a selection of existing colocated partitions to the new nodes/brokers, such that computational load can be distributed within the newly-scaled cluster environment.

For each respective virtual machine (VM) of a plurality of VMs, a distributed computing system generates a unique Application Binary Interface (ABI) for an operating system for the respective VM, compiles a software application to use the unique ABI, and installs the operating system and the compiled software application on the respective VM. A dispatcher node dispatches, to one or more VMs of the plurality of VMs that provide a service and are in the active mode, request messages for the service. Furthermore, a first host device may determine, in response to software in the first VM invoking a system call in a manner inconsistent with the unique ABI for the operating system of the first VM, that a failover event has occurred. Responsive to the failover event, the distributed computing system fails over from the first VM to a second VM.

A method includes determining, by a field instrument in an industrial process and control system, a Highway Addressable Remote Transducer (HART) mode of the field instrument. The method also includes, upon a determination, by the field instrument, that the HART mode is a HART On Demand mode, listening for a HART data signal from a HART master device; when the HART data signal is detected, communicating with the HART master device according to a HART protocol; and when the HART data signal is not detected, diverting a current supply allocated for HART communication to a BLUETOOTH Low Energy (BLE) transceiver for use in BLE communication, and communicating according to a BLE protocol.

Data associated with storage media utilized by one or more storage systems is received. The data is provided as an input to a machine learning model executed by a processing device. The machine learning model identifies one or more deterministic characteristics from the data. The one or more deterministic characteristics associated with the storage media are received from the machine learning model. A data structure comprising the one or more deterministic characteristics is generated for use in a telemetry process to qualify types of storage media.