A technique provides efficient data failover by creation and deployment of a protection policy that ensures maintenance of frequent common snapshots between sites of a multi-site data replication environment. A global constraint optimizer executes on a node of a cluster to create the protection policy for deployment among other nodes of clusters at the sites. Constraints such as protection rules (PRs) specifying, e.g., an amount of tolerable data loss are applied to a category of data designated for failover from a primary site over a network to a plurality of (secondary and tertiary) sites typically located at geographically separated distances. The optimizer processes the PRs to compute parameters such as frequency of snapshot generation and replication among the sites, as well as retention of the latest common snapshot maintained at each site to create a recovery point and configuration of the protection policy that reduces network traffic for efficient use of the network among the sites.

Various aspects provide for implementation of a cloud service for running, monitoring, and maintaining cloud distributed database deployments and in particular examples, provides cloud based services to run, monitor and maintain deployments of the known MongoDB database. Various embodiments provide services, interfaces, and manage provisioning of dedicated servers for the distributed database instances (e.g., MongoDB instances). Further aspects, including providing a database as a cloud service that eliminates the design challenges associated with many distributed database implementations, while allowing the client's input on configuration choices in building the database. In some implementations, clients can simply identity a number of database nodes, capability of the nodes, and within minutes have a fully functioning, scalable, replicated, and secure distributed database in the cloud.

An example method of restoring a database includes obtaining information about backup data of a database from a source storage separate from a compute infrastructure. The information includes a list of data blocks of a file, transferring the data blocks on the list from the source storage to a local storage on the compute infrastructure, and tracking which data blocks of the file have been transferred from the source storage to the local storage concurrently when transferring the data blocks.

Embodiments of the present disclosure include an error recovery method comprising detecting a computing error, restarting a first artificial intelligence processor of a plurality of artificial intelligence processors processing a data set, and loading a model in the artificial intelligence processor, wherein the model corresponds to a same model processed by the plurality of artificial intelligence processors during a previous processing iteration by the plurality of artificial intelligence processors on data from the data set.

One example method includes intercepting an IO issued by an application of a VM, the IO including IO data and IO metadata, storing the IO data in an IO buffer, writing the IO metadata and a pointer, but not the IO data, to a splitter journal in memory, wherein the pointer points to the IO data in the IO buffer, forwarding the IO to storage, and asynchronous with operations occurring along an IO path between the application and storage, evacuating the splitter journal by sending the IO data and the IO metadata from the splitter journal to a replication site.

A global architecture (GLP), as disclosed herein, is based on the thin server architectural pattern; it delivers all its services in the form of web services and there are no user interface components executed on the GLP. Each web service exposed by the GLP is stateless, which allows the GLP to be highly scalable. The GLP is further decomposed into components. Each component is a microservice, making the overall architecture fully decoupled. Each microservice has fail-over nodes and can scale up on demand. This means the GLP has no single point of failure, making the platform both highly scalable and available. The GLP architecture provides the capability to build and deploy a microservice instance for each course-recipient-user combination. Because each student interacts with their own microservice, this makes the GLP scale up to the limit of cloud resources available—i.e. near infinity.

A method for performing a backup operation includes obtaining, by a backup agent, a backup request for a file system, and in response to the backup request: generating a first application partition for an application associated with the file system, performing a dependency analysis on the application to identify application dependency information, populating a first application partition with a copy of the application dependency information and a copy of application data associated with the application, and initiating a storage of a backup to a backup storage system, wherein the backup comprises the first application partition.

The present invention provides a data processing device that includes a memory and includes a first CPU and a second CPU, each having an instruction processing section to process an instruction, a cache to store part of data of the memory, an error detection section to detect an error in the data stored in the cache, and an error correction section to correct the data stored in the cache on the basis of the data stored in the cache and an error notification and output corrected data to the instruction processing section, wherein the error correction section of the first CPU receives, as input, the data stored in the cache of the first CPU, the error notification of the first CPU, the data stored in the cache of the second CPU, and the second error notification, and if the error notification of the first CPU is an error and the error notification of the second CPU is not an error, outputs the data stored in the cache of the second CPU to the instruction processing section of the first CPU, and in other cases, outputs the data stored in the cache of the first CPU to the instruction processing section of the first CPU.

A plug-in based framework provides high availability (HA), including fault tolerance, in a distributed system, such as provided by a virtualized computing environment. The framework uses blueprints that define entities to be monitored, failure conditions, failover actions, restoration actions, and other aspects associated with HA. Microservices execute the blueprints, and a load balancer may balance the execution of the blueprints amongst microservices.

Techniques for implementing RDMA-based recovery of dirty data in remote memory are provided. In one set of embodiments, upon occurrence of a failure at a first (i.e., source) host system, a second (i.e., failover) host system can allocate a new memory region corresponding to a memory region of the source host system and retrieve a baseline copy of the memory region from a storage backend shared by the source and failover host systems. The failover host system can further populate the new memory region with the baseline copy and retrieve one or more dirty page lists for the memory region from the source host system via RDMA, where the one or more dirty page lists identify memory pages in the memory region that include data updates not present in the baseline copy. For each memory page identified in the one or more dirty page lists, the failover host system can then copy the content of that memory page from the memory region of the source host system to the new memory region via RDMA.