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.

Systems and methods for beacon orchestration for concurrent collaboration sessions in peer-to-peer (P2P) or mesh networks are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: detect a first multicast collaboration beacon (MCB) in a P2P or mesh network comprising two or more IHSs; identify a first collaboration session between the two or more IHSs based upon the first MCB; and transmit a second MCB configured, based at least in part upon the first MCB, to orchestrate a second collaboration session in the P2P or mesh network.

A method and apparatus form and/or define a network topology in a Layer 3 network with a plurality of nodes, where each node has at least one interface. To that end, the method defines a plurality of neighborhoods, and assigns at least one interface of each node to at least one of the neighborhoods. The method also assigns a communication role to each interface so that each communication role is effective relative to one of the plurality of neighborhoods. The method then enables communication between the interfaces of the plurality of nodes as a function of the neighborhoods and the communication roles.

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.

Techniques described herein relate to a method for managing nodes. The method may include sending, by a first node of nodes, a node information request to a social manager, where the node information request specifies a portion of a service to be provided to the first node; obtaining node information associated with a portion of the nodes from the social manager, where the portion of the plurality of nodes previously expressed node capability information and node configuration information associated with the portion of the service; identifying a second node of the portion of the nodes based on the node information to perform the portion of the service; and performing the service using the second node, where the second node performs the portion of the service.

A service profile discovery and connection management method for interworking with an IoT device. According to example embodiments, a remote device discovery method for service interworking includes an operation of receiving, from a local device, a discovery profile request message for discovering a device profile of a target local device for service interworking, an operation of searching for a service profile based on a first device profile of the local device included in the discovery profile request message, an operation of searching for, as the device profile of the target local device, a second device profile including one or more functions excluding a function of the local device by matching the searched service profile with device profiles stored in a database, and an operation of transmitting a discovery profile response message including an ID of the second device profile.

A system can a divide database into a group of shards distributed among a group of data centers, wherein the group of shards comprises respective leader replicas. The system can determine respective correlation values between pairs of shards of the group of shards. The system can examine the pairs of shards in a descending order of respective correlation values, comprising, in response to determining that a respective pair of shards of the pairs of shards has a first correlation value greater than a predetermined threshold value, and that at least one shard of the respective pair of shards is unlocked, reassigning leader replicas of the respective pair of shards to be stored in a same data center of the group of data centers, and locking the leader replicas of the respective pair of shards from being reassigned to another data center of the group of data centers during the examining.

An example operation may include one or more of retrieving decentralized identifiers (DIDs) of a plurality of blockchain peers included within a blockchain network, generating a blockchain declarative descriptor (BDD) which uniquely identifies the blockchain network, where the BDD comprises a machine-readable data file with a first field includes the retrieved DIDs of the blockchain network, a second field including signature data of the plurality of blockchain peers, and a third field including metadata, and transmitting the generated BDD to a blockchain network registry.

Aspects discussed herein relate to systems, apparatuses, and methods for providing content distribution via a breadth-first approach for peer-to-peer file sharing in a temporary ad hoc mesh network. For example, a peer-to-peer orchestrator may receive requests for the same asset from multiple mobile devices, determine which of the mobile devices are likely to travel along the same route at the same time, group them together and cause transmission of different asset parts of the requested asset to different mobile devices in the group. If the mobile devices in the group lose connection with the peer-to-peer orchestrator, they may form an ad hoc mesh network and retrieve asset parts from one another. If the group reconnects with a peer-to-peer orchestrator, additional asset parts of the asset may be transmitted to the group and the process may repeat so that each mobile device may obtain each of the asset parts.

A plurality of distributed network nodes may provide a decentralized access gateway to multiple, diverse types of databases. The plurality of distributed network nodes may host a private party blockchain. Each node may execute a peer-to-peer (P2P) client to perform operations associated with the private party blockchain. A subset of the nodes may be configured as validator nodes that may implement gossip protocols to cooperatively validate one or more database operations and generate a new block for the private party blockchain. Another subset of nodes may be configured as host nodes that may receive the new block and update a corresponding local copy of the private party blockchain appending the new block. Utilizing the co-operative validation of database operations and the updates appending the new blocks, the private party blockchain may maintain an immutable digital record of access and updates to the multiple and diverse types of databases.