Methods and systems are described for provisioning cloud-based database systems and performing decoupled maintenance. For example, conventional systems may rely on database management systems to provision and modify databases hosted by a service provider. However, for entities operating complex database systems with the need for highly customized cloud infrastructure, database management systems fail to provide the granular customization and the control necessary to create and service these systems. In contrast, the described solutions provide an improvement over conventional database management system architecture by providing direct communication between an entity and its cloud-based database systems via command line prompts or API calls, decoupling database system maintenance from database system provisioning process to increase the speed and granular customization of the database system. Moreover, the disclosed solution leverages machine learning to predict optimal database system provisioning and maintenance processes and resources.

A system may include a registration module to register the system with a server cluster and a resource collector module operatively connected to the registration module, the resource collector module to identify a list of resources for a container running on the server cluster. The system may also include a resource monitor module operatively connected to the resource collector module, the resource collector module to receive the list of resources for the container, monitor a resource in the list of resources for the container, and generate an event for the container and an event manager module operatively connected to the resource monitor module, the event manager to receive the event and determine a recovery action for the container.

According to aspects of the present disclosure, systems and methods can be provided to recover from memory errors that occur during or following a virtual machine migration. Methods, computer program products and/or systems are provided for handling memory error that perform the following operations: (i) obtaining a memory address that triggered an uncorrected error on a first host associated with a virtual machine migration; (ii) computing a page associated with the memory address; (iii) determining if a copy of the page associated with the memory address is available on a second host associated with the virtual machine migration; (iv) obtaining data from the copy of the page on the second host; and (v) generating a new page on the first host with the data obtained from the second host.

Edge device task management by receiving an indicator corresponding to a first container running a task on a first edge device of a cluster of edge devices, wherein the indicator indicates an error status of the first container, and wherein task data of the task is stored in a first local storage of the first edge device, selecting a second edge device from the cluster of edge devices, wherein a second container on the second edge device is to run the task, instructing the first and second edge devices to transfer the task data from the first local storage of the first edge device to a second local storage of the second edge device, and in response to receiving a notification that indicates the task data has been transferred from the first local storage to the second local storage, sending the task to the second container.

Aspects of the disclosure provide for reducing service disruptions in a computer system. A method of the disclosure may include identifying a plurality of services running on a node of a computer system, determining a plurality of priorities corresponding to the plurality of services, determining a plurality of service capacity factors for the plurality of services in view of the plurality of priorities, and determining a lost impact factor in view of the plurality of service capacity factors.

A software defined (SD) process control system (SDCS) implements controller and other process control-related business logic as logical abstractions (e.g., application layer services executing in containers, VMs, etc.) decoupled from hardware and software computing platform resources. An SD networking layer of the SDCS utilizes process control-specific operating system support services to manage the usage of the computing platform resources and the creation, deletion, modifications, and networking of application layer services with devices disposed in the field environment and with other services, responsive to the requirements and needs of the business logic and dynamically changing conditions of SDCS hardware and/or software assets during run-time of the process plant (such as performance, faults, addition/deletion of hardware and/or software assets, etc.). A visualization system of the SDCS provides a user with a view as to the state of the SDCS as currently configured/running on the computing platform to enable a user to view currently configured interrelationships between logical elements of the control system and other logical and/or physical elements of the control system. The visualization system also provides performance metrics of the system as currently configured to enable a user to understand the operational health of the control system as currently configured.

A method includes receiving a plurality of data processing requests and generating a primary processing stack indicating a queue for processing the first data. The primary processing stack comprises a plurality of layers. Each layer comprises a plurality of slices, wherein each slice represents a portion of the first data of at least one data processing request. The plurality of slices are arranged within each layer based at least on the priority indicator corresponding to the first data that each slice represents. The method further includes receiving resource information about a plurality of servers, assigning each slice of the primary processing stack to one of the servers, and sending processing instructions comprising an identification of each slice of the primary processing stack assigned to the respective server.

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.

Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment, a computer identifies many alternative placements that respectively assign each pluggable database to a respective container database. For each alternative placement, a respective resilience score is calculated for each pluggable database that is based on the container database of the pluggable database. Based on the resilience scores of the pluggable databases for the alternative placements, a particular placement is selected as an optimal placement that would maximize utilization of computer resources, minimize database latencies, maximize system throughput, and maximize the ability of the database cluster to avoid a service outage.