100 results about "Replication (computing)" patented technology

The invention provides a high-scalability consensus method based on a Paxos protocol in a cloud side end environment, and belongs to the technical field of distributed computing, edge computing and cloud computing. The method comprises the following steps of: hierarchically dividing nodes of the distributed consensus system into an upper layer and a lower layer; calculating the weight of an upper layer node; an arbitration set calculation method of an EPaxos protocol is modified, so that the distributed consensus system keeps high availability and high performance; adopting an optimization strategy based on delayed batch submission to optimize a submission strategy of the EPaxos protocol; the proposal replication and leader switching mechanism based on the EPaxos protocol ensures high availability and stability of the distributed consensus system; the weight distribution of the upper layer nodes is dynamically adjusted according to the real-time state and historical performance of the nodes, so that the distributed consensus system can adapt to the change of a network environment and node faults; the throughput and the consensus success rate of the distributed consensus system are improved by optimizing a proposal submission strategy.

The invention provides a file fuzzy copying method and system based on a big data file cluster, and relates to the technical field of file copying, the file fuzzy copying method comprises the steps that file content, file names and metadata feature vectors are extracted from a to-be-matched file set, and the file content feature vectors are obtained based on deep learning model coding; afterwards, a distributed computing framework is used for parallel computing of similarity scores of the to-be-matched files and the files in the target file set, the scores are obtained through weighted computing of file content, file names and metadata feature vector similarity, and a to-be-copied file list is generated through screening according to a preset threshold value. And finally, the distributed file system dynamically allocates a replication task according to system resources, executes file replication based on a data block level parallel transmission mechanism, verifies data integrity and generates a replication task execution report. According to the method, fuzzy file copying can be efficiently and accurately carried out in the big data file cluster, the file copying efficiency and accuracy are improved, and the system resource consumption is reduced.

A scale-out computing cluster may include a large number of computing servers and storage devices. In order to provide high reliability, the computing cluster must be able to handle failures of individual devices. Reliability of the computing cluster may be improved by providing a standby server for each active server in the computing cluster. If any active server fails, the corresponding standby server is activated. The failed server may be brought back online or replaced, at which time the restored server becomes the standby server for the now-active original standby server. During the restoration period, if any other active server fails, the standby server for that active server is immediately activated. As a result, the recovery ability of the computing cluster is only challenged if both servers of an active / standby pair fail during the restoration period, substantially improving reliability.

A computing system performs real-time replication of database management system transactions. The computing system includes a source DBMS, a replication service and a data lakehouse. The source DBMS stores at least one source transaction table recording at least one source transaction and generates at least one recovery log indicating at least one modification in the at least one source transaction table. The replication service replicates the at least one source transaction recorded in the at least one source transaction table by generating at least one data file having a first data format. The data lakehouse stores at least one lakehouse table corresponding to the at least one source transaction table and having a second data format different from the first data format, and to modify the at least one lakehouse table based on the at least one data file.

Systems and methods include an Information Handling System (IHS) that is adapted to diagnose root causes of issues reported by hardware and / or software of the IHS. Telemetry is monitored that specifies operating status information for hardware components of the IHS. Stress event are detected that are related to the hardware components. One or more stress tests are identified for evaluation of each hardware component that is related to stress event. While monitoring the telemetry, the stress tests are conducted in order to replicate the detected stress event. When the detected stress event is replicated, a root cause hardware component of the IHS is determined based on machine learning evaluation of the telemetry generated during replication of the stress event.

The present invention relates to a VDF (initial state, t), comprising: a one-way invertible function S component directly or indirectly constructing the VDF, wherein the S component is defined as an invertible function where a computation delay of forward logic is significantly greater than that of reverse logic; a computation phase: a intermediate states, and / or F(A0, Ab)=S(A0⊕Ab, △) indirectly constructing the VDF; and a verification phase: receiving the initial state and a+1 states, preferably completing a+1 VDF-1 verification tasks by means of parallel (a+1)*(b?b:1) S-1 logic verifications. A VDF construction with salt is preferred; the S component preferably has a structure of F(A, B)=g(A⊕B), wherein g-1 uses sparse tap design. Disclosed are proposed industrial standards VDF32 and VDF8192. The present invention achieves the effects of no initialization parameter phase (Setup); quantum-resistant design; and entropy preservation. Disclosed related uses comprise a randomness beacon, a portal website, a proof-of-replication, a storage service terminal, a cloud computing terminal, two types of blockchain systems, Token encoding and verification, and automatic commitment opening. To support industrial implementation, further disclosed are a VDF computing apparatus, a VDF verification apparatus, a medium, and a chip.

In some examples, a first computing system receives a first metadata change made to metadata of a first instance of an object stored at a storage location of the first computing device. The first computing system associates, with the first metadata change, a time of the first metadata change. Based on receiving replication of a second instance of the object from a second computing system, the first computing system determines that a second metadata change was made to metadata of the second instance of the object. Further, the second instance of the object includes a time of the second metadata change. The first computing system at least one of: resolves a conflict between the first metadata change and the second metadata change based at least on comparing the times, or merges the first metadata change with the second metadata change into the metadata for the object.

A scale-out computing cluster may include a large number of computing servers and storage devices. In order to provide high reliability, the computing cluster must be able to handle failures of individual devices. Reliability of the computing cluster may be improved by providing a standby server for each active server in the computing cluster. If any active server fails, the corresponding standby server is activated. The failed server may be brought back online or replaced, at which time the restored server becomes the standby server for the now-active original standby server. During the restoration period, if any other active server fails, the standby server for that active server is immediately activated. As a result, the recovery ability of the computing cluster is only challenged if both servers of an active / standby pair fail during the restoration period, substantially improving reliability.

Methods, systems, and devices for data management are described. A first computing node within a first set of computing nodes may receive, from a network gateway, a first replication request that is associated with a first replication job for the first cluster of computing nodes to provide copies of a first set of one or more computing snapshots to a second cluster of computing nodes. The first set of computing nodes may determine that a second computing node different from the first computing node will service the data replication request. A network bandwidth allocation may be increased for the first computing node that received the replication request directly from the network gateway while the network bandwidth allocation for the second computing node is maintained for the second computing node based on the second computing node receiving the request via an internal redirection of the request.

Techniques for remote computing are described. In some examples, a decorator call in source code is used to generate a remote job to be processed by a provider network using a runtime environment that replicates a runtime environment where the source code was developed. The runtime environment is replicated using information such as runtime dependencies, packages, and environment variables of the development environment.

Systems and methods for providing selective data-replication among nodes of a distributed multi-tenancy MSSP architecture for performing secure orchestration and automated response (SOAR) are provided. According to one embodiment a master SOAR node of an MSSP receives multiple messages via a secure router coupling a computing environment of the MSSP in communication with respective computing environments of multiple customers of the MSSP. The messages contain information regarding alerts relating to network infrastructure of the customers and the information is controlled by data sharing policies implemented by tenant SOAR nodes within the respective computing environments of the customers. Based on an investigation into an alert relating to a network infrastructure of a particular customer, the master SOAR node causes a workflow to be remotely executed by a tenant SOAR node within the computing environment of the particular customer.

Verifying that data has been correctly replicated to a replication target, including: replicating a dataset stored at a first computing system to a second computing system; and determining, based at least on a comparison of a first hash and a second hash, validity of the dataset stored at the second computing system, wherein the first hash is generated by applying a hash function to a copy of the dataset that is stored at the first computing system and the second hash is generated by applying the hash function to a copy of the dataset that is stored at the second computing system.

A computing device accesses a current workload. The computing device saves the current workload into a computer workload accessible format, the computer workload accessible format usable for replicating the current workload. Security testing may be performed using the computer workload accessible format.

A power state framework executes a workload in supported computing device types and configurations during a sequence of power state transitions to simulate user experiences. Workloads can be combined, e.g., default workload plus add-on workload(s). Device states are monitored, collected, reported, and analyzed to identify faults, which are prioritized for repair, e.g., based on the projected impact of the faults on users, e.g., if the users were to experience the faults. Computing device states include states of attached peripheral devices and wireless or wired connectivity before and after power state transitions. Device state indicates device stability throughout supported device power state transitions. Devices under test can be on debuggers during execution of workload and power state transitions. Automated, accelerated simulation of the execution of end user workloads and power state transitions in actual computing devices saves time and avoids replication errors compared to manual user-driven device testing.

The invention discloses a switching method and system of a redundant server, and solves the problems of detection delay and data inconsistency in traditional switching through cooperative work of hardware layer redundancy (redundant components, multi-path architecture and power supply protection), intelligent fault detection (multi-dimensional monitoring and second-level heartbeat) and dynamic load balancing and data consistency guarantee (synchronous replication and seamless switching technology). Experiments show that the scheme shortens the annual downtime of the system, improves the availability, is suitable for key business scenes such as financial transactions and cloud computing, and ensures the service continuity and the data reliability.

One exemplary operation may include one or more of: receiving a message from a proxy installed at a data replication server, the message including a check and validated status identifier of a data replication operation, identifying a latency value associated with the data replication server, and determining whether a data loss has occurred based on the checksum verified state identifier and the latency value, and in response to determining that the data loss has occurred, sending a notification of the data loss to a computing system associated with the data replication server.

The invention relates to the technical field of communication, in particular to a resource management method and device, equipment and a medium, and the method comprises the steps: obtaining a task sequence hardware computing chain resource request, used for indicating a request demand, of application software; matching a hardware computing link corresponding to the task sequence from the mapping relation according to the hardware computing link resource request; returning an operation handle of the hardware calculation link to the application software; after a data processing request obtained by writing to-be-processed data into the operation handle by application software is received, processing the to-be-processed data in sequence according to the hardware calculation link; application software only needs to copy the to-be-processed data once, so that data copying from a large amount of memory to endpoint equipment is omitted; when the data is processed on the endpoint device, the link information of the hardware calculation link is carried, and the data can be output to the next endpoint device according to the calculation link information after calculation is completed, so that a large amount of path occupation and memory replication are reduced, and the utilization rate of various resources is improved.

The present invention discloses a dynamic task replication method, device and system in an edge computing environment. The method includes: establishing an optimization problem with the goal of minimizing regret, which is the difference between the total completion time of jobs in the edge environment and the total delay of job completion under the ideal optimal replication decision; using a task replication decision algorithm based on multi-armed bandits to solve the optimization problem, including: at the beginning of the first time slot, estimating the task computation amount w according to the task type of the task and the size of the input data t ; for each task t, calculating the lower confidence bound of the delay of replicating task t from edge cluster i to edge cluster j #imgabs0# According to #imgabs1# determine all available edge clusters, and select r t smaller available edge clusters as target edge clusters, and replicate the task to all target edge clusters for execution. The present invention applies the algorithm based on multi-armed bandits to the task replication problem of the edge computing system for the first time, and the method has excellent delay performance and service reliability.

According to some embodiments, a digital core for a cloud computing environment is associated with an enterprise (e.g., the digital core may comprise enterprise resource planning software optimized for an in-memory database). Moreover, an integration inspector framework may establish a checkpoint between an application system (e.g., a source application system to provide information via the digital core and / or a target application system to receive information via the digital core) and the digital core. The integration inspector framework then automatically validates a communication arrangement associated with the application system and performs a data replication test, in connection with the application system and the digital core, using test data. A result of the data replication test is then evaluated and indicated (e.g., to a user via a user interface).

Disclosed techniques and solutions can provide improved snapshot replication. Typically, an initial replica obtained using snapshot replication is periodically updated. However, the update process can unnecessarily consume computing resources if data in a source data object has not changed with respect to data in a replica data object. Disclosed techniques check to determine whether a snapshot replica is out of date before obtaining a new snapshot. The checks can be performed on manual request or on the occurrence of triggers, such as receiving a query that accesses the replica data object or according to a schedule. Information for current and prior versions of the remote data object can be compared to determine whether a replica is out of date, such as digest values of contents of the remote data object or timestamps associated with the remote data object.

A power state framework executes a workload in supported computing device types and configurations during a sequence of power state transitions to simulate user experiences. Workloads can be combined, e.g., default workload plus add-on workload(s). Device states are monitored, collected, reported, and analyzed to identify faults, which are prioritized for repair, e.g., based on the projected impact of the faults on users, e.g., if the users were to experience the faults. Computing device states include states of attached peripheral devices and wireless or wired connectivity before and after power state transitions. Device state indicates device stability throughout supported device power state transitions. Devices under test can be on debuggers during execution of workload and power state transitions. Automated, accelerated simulation of the execution of end user workloads and power state transitions in actual computing devices saves time and avoids replication errors compared to manual user-driven device testing.

The present application belongs to the technical field of IT software development, and relates to a rapid cloning method for a distributed PostgreSQL database. The method comprises the following steps: creating a distributed cluster consistent restore-point; performing snapshot-based cloning on underlying database instances; restoring an underlying database by means of the restore point; and performing metadata restoration. In the present application, on the basis of the study on the principle of a Citus plug-in, a metadata repair script is customized, which is used as a post step following the restoration of an underlying database, and realizes a cluster-dimension cloning operation, and thus metadata of a cluster obtained after physical cloning replication of a distributed database which is obtained on the basis of extension performed by Citus is directly repairable, and it is not necessary to sequentially execute, according to a conventional process, the operations of newly building an empty cluster, calling a Citus interface by the new cluster to regenerate metadata and performing logic importing of service data, thereby significantly shortening a cloning time; moreover, in view of the general capability of a cloud computing infrastructure layer, a Citus open interface and a PostgreSQL self-restoration mechanism, the cloning operation for an underlying database is realized.

The application discloses a method for automatically discovering block devices and copying changed data in real time and a storage medium, belongs to the technical field of cloud computing, and aims to solve the technical problem of how to realize automatic discovery of block devices and real-time copying of data of newly added disks, reduce the invasiveness to user servers, and increase the continuity of user services. The technical scheme is as follows: the method is to load a block device data real-time copying kernel module into a linux kernel, register block device information, poll and scan or actively trigger by listening to block device events to query newly mounted / dismounted disks, enable / disable the real-time copying function of the corresponding disks, and simultaneously, in the case of not interrupting read-write I / O, real-time copy the disks in use.

Disclosure is made of methods, apparatus and system for migrating virtual machines (VMs) between source and destination in a computing environment and, more specifically, to replication based migration. VMs migration is controlled so as to manage transferal of data associated with one or more VMs from a source location to a destination location to meet certain user definable or system constraints. Dynamic control and adjustment of system parameters associated with the migration is also disclosed.

A disclosed computing device capable of achieving high data rates in link diagnostic testing by replicating traffic may include (1) a packet forwarding device configured to generate, as part of a link diagnostic test, traffic that falls below a certain threshold by a deficit and (2) a packet replication device configured to (A) receive at least a portion of the traffic from the packet forwarding device and (B) replicate the portion of traffic to generate supplemental traffic that compensates for the deficit, and (3) an interface configured to transmit the traffic and the supplemental traffic to a remote device via a link as part of the link diagnostic test. Various other apparatuses, systems, and methods are also disclosed.

Embodiments of the present application disclose a metadata backup recovery method and device, electronic equipment and a storage medium, wherein the method is applied to a computing node in a distributed database management system, comprising: starting the computing node, the computing node storing a metadata table, used for recording a replication position of the computing node in a DDL log through coordinate data; pulling the DDL log from a metadata node according to the coordinate data, executing a DDL command in the DDL log to update local metadata of the computing node, and updating the coordinate data of the metadata table, so that the recovery and reconstruction of the distributed database computing node can be conveniently and accurately realized by using the local transaction processing capability of the computing node.

A computer can connect with a middleware computing system. The middleware computing system may use application programming interfaces (APIs), webhooks, file-based integration, database replication, message queues, websockets, or direct integration to establish connections with different computing devices. The computer may request a data structure classification for an external data structure stored in a remote computing device. The middleware computing system can receive the request, identify the connection that the middleware computing device has with the remote computing device, and retrieve records for transactions performed by the external data structure from the remote computing device. The middleware computing system can use metadata in the records to automatically determine a data structure type of the data structure. The middleware computing system can generate instructions that cause the computer to link the external data structure with the profile.

An example operation may include one or more of receiving a message from an agent installed at a data replication server, the message comprising a status identifier of a checksum validation of a data

A computer can connect with a middleware computing system. The middleware computing system may use application programming interfaces (APIs), webhooks, file-based integration, database replication, message queues, websockets, or direct integration to establish connections with different computing devices. The computer may request a data structure classification for an external data structure stored in a remote computing device. The middleware computing system can receive the request, identify the connection that the middleware computing device has with the remote computing device, and retrieve records for transactions performed by the external data structure from the remote computing device. The middleware computing system can use metadata in the records to automatically determine a data structure type of the data structure. The middleware computing system can generate instructions that cause the computer to link the external data structure with the profile.

The application belongs to the technical field of artificial intelligence, graph data processing and high-performance computing, and discloses a communication-efficient memory fusion method and system for super-memory graph neural network training and a medium. A task-specific memory management scheme, zero-copy CPU-GPU transmission, a layout-aware GPU intercommunication pipeline and an out-neighbor gradient aggregation mechanism are used to significantly reduce the overall data communication overhead while ensuring training accuracy. In the fusion memory-aware graph partitioning, a same subgraph is maintained by the storage layout of multiple GPUs. For P task partitioning characteristics and T task partitioning vertex dimensions, the number of graph partitions required by the system is reduced, thereby reducing neighbor replication, and the overall CPU-GPU communication overhead of the system is successfully reduced by 50% to 64%. The back propagation of the out-neighbor gradient aggregation of the application changes the back propagation into an out-neighbor aggregation process in the preprocessing stage.