A personal blockchain is generated as a cloud-based software service in a blockchain environment. The personal blockchain immutably archives usage of any device, perhaps as requested by a user. However, some of the usage may be authorized for public disclosure, while other usage may be designated as private and restricted from public disclosure. The public disclosure may permit public ledgering by still other blockchains, thus providing two-way public/private ledgering for improved record keeping. Private usage, though, may only be documented by the personal blockchain.

A personal blockchain is generated as a cloud-based software service in a blockchain environment. The personal blockchain immutably archives usage of any device, perhaps as requested by a user. However, some of the usage may be authorized for public disclosure, while other usage may be designated as private and restricted from public disclosure. The public disclosure may permit public ledgering by still other blockchains, thus providing two-way public/private ledgering for improved record keeping. Private usage, though, may only be documented by the personal blockchain.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Software that may be implemented using a circuit is disclosed. The software may include an Application Programming Interface (API) to receive a request from an application relating to a key-value pair for a Key-Value Solid State Drive (KV-SSD). The key-value pair may include a key and a value; the application may be executed by a processor. The software may also include combiner software to combine the key with an index to produce an indexed key, and execution software to execute an operation on the KV-SSD using the indexed key and the value.

Software that may be implemented using a circuit is disclosed. The software may include an Application Programming Interface (API) to receive a request from an application relating to a key-value pair for a Key-Value Solid State Drive (KV-SSD). The key-value pair may include a key and a value; the application may be executed by a processor. The software may also include combiner software to combine the key with an index to produce an indexed key, and execution software to execute an operation on the KV-SSD using the indexed key and the value.

Approaches for data processing are disclosed that include receiving, from a client, an application programming interface (API) request at an API endpoint of an API, where the API endpoint is configured to process data requests at a data plane of the API, identifying, from a header of the API request, a request for metadata associated with the API, redirecting the API request to a metadata plane of the API, retrieving, at the metadata plane of the API, the requested metadata based on the header of the API request, and transmitting, via the API endpoint and to the client, a response message indicating the requested metadata.

Approaches for data processing are disclosed that include receiving, from a client, an application programming interface (API) request at an API endpoint of an API, where the API endpoint is configured to process data requests at a data plane of the API, identifying, from a header of the API request, a request for metadata associated with the API, redirecting the API request to a metadata plane of the API, retrieving, at the metadata plane of the API, the requested metadata based on the header of the API request, and transmitting, via the API endpoint and to the client, a response message indicating the requested metadata.

A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a set of input nodes and a set of criteria. The acts can include obtaining an adjacency list representing a large connected component. The large connected component can include nodes, edges, and edge metadata. A quantity of the nodes of the large connected component can exceed 1 billion. The adjacency list can be distributed across the multiple processing nodes. The nodes of the large connected component can include the input nodes. The acts also can include performing one or more iterations of traversing the large connected component until a stopping condition is satisfied. Each iteration can include processing a set of input nodes at the multiple processing nodes using the set of criteria to generate first data at the multiple processing nodes, determining a set of output nodes such that each output node of the set of output nodes is one hop from a respective input node of the set of input nodes, consolidating the first data from the multiple processing nodes to a first processing node of the multiple processing nodes, processing the first data at the first processing node; and assigning the set of input nodes for a subsequent iteration of the one or more iterations based on the set of output nodes when the stopping condition is not satisfied. The acts further can include outputting second data based on the first data received and processed at the first processing node during the one or more iterations. Other embodiments are disclosed.

A distributed system including multiple processing nodes. The distributed system can perform certain acts. The acts can include receiving a set of input nodes and a set of criteria. The acts can include obtaining an adjacency list representing a large connected component. The large connected component can include nodes, edges, and edge metadata. A quantity of the nodes of the large connected component can exceed 1 billion. The adjacency list can be distributed across the multiple processing nodes. The nodes of the large connected component can include the input nodes. The acts also can include performing one or more iterations of traversing the large connected component until a stopping condition is satisfied. Each iteration can include processing a set of input nodes at the multiple processing nodes using the set of criteria to generate first data at the multiple processing nodes, determining a set of output nodes such that each output node of the set of output nodes is one hop from a respective input node of the set of input nodes, consolidating the first data from the multiple processing nodes to a first processing node of the multiple processing nodes, processing the first data at the first processing node; and assigning the set of input nodes for a subsequent iteration of the one or more iterations based on the set of output nodes when the stopping condition is not satisfied. The acts further can include outputting second data based on the first data received and processed at the first processing node during the one or more iterations. Other embodiments are disclosed.