The present disclosure discloses a method, device and node apparatus for blockchain permission control. The method comprises: receiving target information sent by a node apparatus; acquiring an account address of a configured account of the node apparatus; acquiring, according to the account address, from a predetermined block stored with the account address and an account permission corresponding to the account address, the account permission corresponding to the account address; and processing the target information according to the account permission accordingly. In the present disclosure, node apparatuses are configured with corresponding accounts, and performing permission control on the accounts can restrict permissions of different node apparatuses so as to ensure security and privacy of blockchain data.

There is provided an information processing apparatus including a processor that obtains, from a first P2P database, evaluation information for evaluating learning of a user, which is obtained by an acquisition device, and performs evaluation on learning performed by the user on the basis of the evaluation information.

There is provided an information processing apparatus including a processor that obtains, from a first P2P database, evaluation information for evaluating learning of a user, which is obtained by an acquisition device, and performs evaluation on learning performed by the user on the basis of the evaluation information.

Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Methods and systems for managing distributed systems are disclosed. The distributed system may include any number of data processing systems that may contribute to the functionality of the distributed system. To contribute to the functionality of the distributed system, each of the data processing systems may need to be configured to facilitate cooperative operation. To manage configuration of data processing system, a control plane may be utilized. The control plane may utilize a consensus based process for managing leadership among members of the control plane.

A storage node of a database replica group may distribute different portions of data in local storage and external storage, where local storage and external storage are organized using different types of index structures. Responsive to receiving an access request for a database, a storage node may determine that an item of the database to be accessed by the request does not reside within a first portion of the database stored locally at the storage node. Responsive to this determination, the storage node may obtain from an external storage service a second portion of the database, the second portion including a plurality of items including the item, and the second portion organized according to a structure different from the first portion. The storage node may then store the plurality of obtained items in the first portion and process the request using the first portion of the database.

Systems, computer program products, and methods are described herein for identification and tracking of device configuration parameters in a distributed network. The present invention is configured to determine that a first user is authorized to execute resource transfers within a distributed network; retrieve at least device configuration parameters associated with the first user input device; query, using an information retrieval engine, metadata associated with one or more user input devices within the distributed network for traceable instances of digital activities of the first user input device; determine, from at least one of the one or more user input devices, one or more traceable instances of digital activities associated with the first user input device; in response, populate an internal database associated with the at least one user device with at least a resource distribution account information associated with the first user.

A peer-to-peer network of printing devices is configured to share printing device information, such as job queue status, configuration information, and resource information for automated load balancing, capability matching, failover, and print splitting operations. A digital front end (DFE) of a printing device establishes connections with the DFEs of other printing devices to manage output within the peer-to-peer network in an automated manner. When a print job is received at a DFE for a printing device, it is evaluated to determine how to best manage the output for the print job.

A peer-to-peer network of printing devices is configured to share printing device information, such as job queue status, configuration information, and resource information for automated load balancing, capability matching, failover, and print splitting operations. A digital front end (DFE) of a printing device establishes connections with the DFEs of other printing devices to manage output within the peer-to-peer network in an automated manner. When a print job is received at a DFE for a printing device, it is evaluated to determine how to best manage the output for the print job.

A method includes generating first messages having a first message type, at a first subset of network nodes in a peer-to-peer network, based on requests received from a first external computing device; submitting second events to the peer-to-peer network, the first events having payloads that are based on the first message type; and using at least one directed acyclic graph (DAG) to attempt to reach a consensus about timestamps in the first events. The method also includes receiving, at a second subset of network nodes, events having different payload types from the peer-to-peer network and filtering for first events having payloads based on the first message type; and when one of the second subset of network nodes receives a first event having a payload that is based on the first message type, converting the payload into a second message that is sent to a second external computing device.