Disclosed embodiments relate to reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server. Operations may include receiving operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs; generating, through a machine learning process, a statistical model of the operational data; receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle; identifying an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data; and wirelessly sending a report to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error.

This disclosure describes techniques for providing early acknowledgments to a source device performing a data write operation within a data center or across a geographically-distributed data center. In one example, this disclosure describes a method that includes receiving, by a gateway device and from a source device within a local data center, data to be stored at a remote destination device that is located within a remote data center; storing, by the gateway device, the data to high-speed memory included within the gateway device; transmitting, by the gateway device, the data over a connection to the remote data center; after transmitting the data and before the data is stored at the remote destination device, outputting, by the gateway device to the source device, a local acknowledgment, wherein the local acknowledgment indicates to the source device that the data can be assumed to have been stored at the remote destination device.

A workgroup-computing-entity-based fail-safe/evolvable hardware core structure is disclosed which includes a 3-hierarchical-level 6-workgroup-Basic-Building-Block (6-wBBB) created to supplant the node-computing-entity-based non-fail-safe/limited evolvable von-Neumann core structure of 3-hierarchical-level 3-node-BBB, (i.e., base-level IO-devices/mid-level main memory/top-level CPU) and all the first-time fail-safe workgroup systems can be subsequently generated in the second period along the workgroup-computing evolutionary timeline. Furthermore, based on the first 6-wBBB evolvable architecture, the workgroup evolutionary processes can go up to 7 generations in creating all the necessary workgroup-computing entity-based hardware core structures, so that all the real-time intelligent workgroup-computing systems can be generated in the third period along the workgroup-computing evolutionary timeline.

Distributed processors and methods for compiling code for execution by distributed processors are disclosed. In one implementation, a distributed processor may include a substrate; a memory array disposed on the substrate; and a processing array disposed on the substrate. The memory array may include a plurality of discrete memory banks, and the processing array may include a plurality of processor subunits, each one of the processor subunits being associated with a corresponding, dedicated one of the plurality of discrete memory banks. The distributed processor may further include a first plurality of buses, each connecting one of the plurality of processor subunits to its corresponding, dedicated memory bank, and a second plurality of buses, each connecting one of the plurality of processor subunits to another of the plurality of processor subunits.

Distributed processors and methods for compiling code for execution by distributed processors are disclosed. In one implementation, a distributed processor may include a substrate; a memory array disposed on the substrate; and a processing array disposed on the substrate. The memory array may include a plurality of discrete memory banks, and the processing array may include a plurality of processor subunits, each one of the processor subunits being associated with a corresponding, dedicated one of the plurality of discrete memory banks. The distributed processor may further include a first plurality of buses, each connecting one of the plurality of processor subunits to its corresponding, dedicated memory bank, and a second plurality of buses, each connecting one of the plurality of processor subunits to another of the plurality of processor subunits.

Disclosed embodiments relate to adjusting vehicle Electronic Control Unit (ECU) software versions. Operations may include receiving a prompt to adjust an ECU of a vehicle from executing a first version of ECU software to a second version of ECU software; configuring, in response to the prompt and based on a delta file corresponding to the second version of ECU software, the second version of ECU software on the ECU in the vehicle for execution; and configuring, in response to the prompt, the first version of ECU software on the ECU in the vehicle to become non-executable.

In one embodiment, a telemetry system may track a cloud synchronization system to improve performance. A service proxy 114 may receive a matching file metadata set 304 for a matching file 134 stored in a cloud user account 132 of a cloud synchronization system. The service proxy 114 may execute a synchronization verification of the matching file metadata set 304 to a local file 112 stored in a client device 110. The service proxy 114 may create a telemetry report 400 describing a synchronization error 412 as determined by the synchronization verification.

A plurality of storage nodes within a single chassis is provided. The plurality of storage nodes is configured to communicate together as a storage cluster. The plurality of storage nodes has a non-volatile solid-state storage for user data storage. The plurality of storage nodes is configured to distribute the user data and metadata associated with the user data throughout the plurality of storage nodes, with erasure coding of the user data. The plurality of storage nodes is configured to recover from failure of two of the plurality of storage nodes by applying the erasure coding to the user data from a remainder of the plurality of storage nodes. The plurality of storage nodes is configured to detect an error and engage in an error recovery via one of a processor of one of the plurality of storage nodes, a processor of the non-volatile solid state storage, or the flash memory.

The illustrative systems and methods use a special-purpose volume-replicating server(s) to offload client computing devices operating in a production environment. The production environment may remain relatively undisturbed while production data is replicated to a geographically distinct destination. Replication is based in part on hardware-based snapshots generated by a storage array that houses production data. The illustrative volume-replicating server efficiently moves data from snapshots on a source storage array to a destination storage array by transferring only changed blocks for each successive snapshot, i.e., transferring incremental block-level changes. Periodic restore jobs may be executed by destination clients to keep current with their corresponding source production clients. Accordingly, after the source data center goes offline, production data may be speedily restored at the destination data center after experiencing only minimal downtime of production resources. By employing block-level techniques, the disclosed solutions avoid the file-based data management approaches of the prior art, which tend to be too time-consuming and resource-intensive for the present scenario.

The illustrative systems and methods use a special-purpose volume-replicating server(s) to offload client computing devices operating in a production environment. The production environment may remain relatively undisturbed while production data is replicated to a geographically distinct destination. Replication is based in part on hardware-based snapshots generated by a storage array that houses production data. The illustrative volume-replicating server efficiently moves data from snapshots on a source storage array to a destination storage array by transferring only changed blocks for each successive snapshot, i.e., transferring incremental block-level changes. Periodic restore jobs may be executed by destination clients to keep current with their corresponding source production clients. Accordingly, after the source data center goes offline, production data may be speedily restored at the destination data center after experiencing only minimal downtime of production resources. By employing block-level techniques, the disclosed solutions avoid the file-based data management approaches of the prior art, which tend to be too time-consuming and resource-intensive for the present scenario.