Disclosed are various implementations of cluster capacity management for infrastructure updates. In some examples, cluster hosts for a cluster can be scheduled for an update. A component of a datacenter level resource scheduler can analyze cluster specific resource usage data to identify a cluster scaling decision for the cluster. The datacenter level resource scheduler transmits an indication that the resource scheduler is successfully invoked. Cluster hosts can then be updated.

Methods and systems for implementing an abstraction layer for streaming data sources are disclosed. A request to perform an operation based on one or more keys is received using a key-value interface. A streaming data source is selected based on the request. The operation is performed using the streaming data source, wherein the operation comprises storing or retrieving one or more values based on the one or more keys.

A device may include a plurality of data processing engines. Each of the data processing engines may include a memory pool having a plurality of memory banks, a plurality of cores each coupled to the memory pool and configured to access the plurality of memory banks, a memory mapped switch coupled to the memory pool and a memory mapped switch of at least one neighboring data processing engine, and a stream switch coupled to each of the plurality of cores and to a stream switch of the at least one neighboring data processing engine.

Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.

In an application execution system having a plurality of application servers, each application server stores a plurality of applications, and has computational resources for executing applications in response to received requests. Each application server also includes instructions for loading a respective application into volatile storage and executing the application in response to a request from a client, and for returning a result. A generic application instance may be cloned, creating a pool of generic application instance clones that can be loaded with code for a requested application to produce an application instance. The application instance can then be stored in a cache to be used for a future application request.

A computer-implemented method upgrades an edge network based on analysis by a learning model. The method includes identifying, in a network, a plurality of devices, where each device in the network is configured to provide data on at least one other device in the network. The method also includes determining capabilities of each device of the plurality of devices. The method further includes monitoring, for each device, capacity information and tasks performed during operation of the network. The method includes analyzing, based on the monitoring, each use of each device. The method also includes recommending, in response to the analyzing and by a learning model, a first upgrade to the network. The method further includes implementing the first upgrade.

A device is configured to detect a triggering event within a network that is associated with a communication error between a first network device and a second network device. The device is further configured to identify a first node in a computer network map corresponding with the first network device and to identify node properties for the first node. The device is further configured to identify the error correction instructions in the node properties for the first node that include an address for rerouting data traffic to a third network device. The device is further configured to apply the error correction instructions where applying the error correction instructions suspends data traffic to the second network device and reroutes data traffic to the third network device.

Disclosed are a network control device and an operation method of the network control device for dynamically constructing an end-to-end virtual dedicated network slice based on a software-defined network (SDN) over the entire wired and wireless network section of a private network and a public network.

A method and system elects a master node from a plurality of nodes in a distributed system. A serverless elector function periodically outputs an election API call to a load balancer. The load balancer elects a master node from a plurality of candidate nodes each time the load balancer receives the election API call.

The present disclosure relates to systems and methods for providing cloud-based services securely to on-premises networks or other infrastructure. More particularly, the present disclosure relates to systems and methods for enriching first-party data (e.g., data collected directly by an on-premises server) stored within on-premises networks by enabling the on-premises networks to retrieve and process third-party data stored on cloud-based networks. As a technical benefit, cloud-based services can be performed on the first-party data within the on-premises networks.