In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource. The micro-capability is executed and configured on the computing resource, and an output of the micro-capability is provided to the target environment.

In another aspect, a system for machine learning using a distributed framework, includes a computing device communicatively connected to a plurality of remote devices, the computing device designed and configured to select at least a remote device of a plurality of remote devices, determine a confidence level of the at least a remote device, and assign at least a machine-learning task to the at least a remote device, wherein assigning further comprises assigning at least a secure data storage task to the at least a remote device and assigning at least a model-generation task to the at least a remote device.

Embodiments of a system and method are described for generating and distributing programming to mobile devices over a network. Devices are provided with Players specific to each device and Applications that are device independent. Embodiments include a full-featured WYSIWYG authoring environment, including the ability to bind web components to objects.

Fault-tolerant and highly available configuration of distributed services including a computer-implemented method for role-based configuration discovery comprising receiving a request comprising an identifier of a role; identifying a first key, in a replica of a distributed configuration store, comprising a first value that matches the role identifier; identifying one or more other key-value pairs associated in the replica with the first key; and returning a response to an entity that sent the request comprising the value of at least one key-value pair that is specific to the role the service has. Also disclosed are techniques for log forwarding.

A method and system for reconciliation of a set of K8s resource/objects includes a Graph Dependency (GD) API enabling a Developer to define a dependency graph between K8s resource/objects of the set. Interfacing with the API Server in a K8s system, the GD API monitors events on the K8s resource/objects of the set, and upon occurrence of an event on an affected K8s resource/object, triggers reconciliation of the set as determined necessary based on the matching of versioning level of the affected K8s resource/object, with the versioning level of the parent recorded in children of the affected K8s resource/object, from immediate children to leaf children in the dependency graph.

A computing system includes a computing device configured to select a remote app to be launched based on user input, and display a history record of the selected remote app. The history record provides a preview of items previously handled by the user with the remote app. One of the items is selected based on user input. An operation record server is configured to store app history data provided by the remote app corresponding to the items previously handled by the user with the remote app. The remote app is launched with the app history data associated with the selected item so that the remote app is restored back to the item previously handled by the user.

A plurality of containers can be configured for running applications associated to at least one node of a distributed computing environments. The containers of the plurality of containers includes integrated intelligence that provides an in memory state component that detects how container instances are running. A quorum synchronization component of the integrated intelligence can coordinate the activities of the containers. A first container can be initiated for running a first node application. The memory state component can determine if a topology exists in the plurality of containers that is running an existing application matching the first node application. The quorum synchronization component of the integrated intelligence can coordinate running of the first node application with the first container with the existing application.

A service processing method and a network device, where the network device includes a network processor (NP) and a central processing unit (CPU). The NP is configured to receive a first packet, obtain a packet feature of the first packet, obtain a processing rule corresponding to the packet feature, process the first packet based on the processing rule, to obtain a second packet, and send the second packet to the CPU. The CPU is configured to receive the second packet, and process the second packet. Before the CPU processes the first packet, the NP processes the first packet, and sends the second packet obtained after processing to the CPU.

In accordance with embodiments disclosed herein, there are provided mechanisms and methods for automating deployment of applications in a multi-tenant database environment. For example, in one embodiment, mechanisms include managing a plurality of machines operating as a machine farm within a datacenter by executing an agent provisioning script at a control hub, instructing the plurality of machines to download and instantiate a lightweight agent; pushing a plurality of URL (Uniform Resource Locator) references from the control hub to the instantiated lightweight agent on each of the plurality of machines specifying one or more applications to be provisioned and one or more dependencies for each of the applications; and loading, via the lightweight agent at each of the plurality of machines, the one or more applications and the one or more dependencies for each of the one or more applications into memory of each respective machine.

Flags for a command-line interface (CLI) can be automatically determined. In one example, a system can receive a user input through the CLI to manipulate an object in a computing environment. The user input can include a flag for setting a customizable parameter of the object to a particular value. The system can also receive definition data specifying one or more customizable parameters for the object. The system can then determine one or more available flags associated with the one or more customizable parameters specified in the definition data, where the available flag(s) are usable for configuring the one or more customizable parameters of the object. Based on the available flag(s), the system can determine if the flag in the user input is valid. If so, the system can manipulate the object in the computing environment such that the manipulated object has the particular value for the customizable parameter.