In one or more embodiments, one or more methods, processes, and/or systems may modify a configuration of an information handling system (IHS) to prevent access of a first non-volatile memory medium, associated with the IHS, that stores a recovery operating system; may boot the information handling system from a second non-volatile memory medium of the IHS; may determine that at least one issue associated with a boot sequence has occurred; may modify the configuration of the IHS to provide access of the first non-volatile memory medium; may modify the configuration of the IHS to boot the information handling system from the first non-volatile memory medium; may restart the IHS; and may boot the recovery operating system from the first non-volatile memory medium.

To generate a checkpoint for a virtual machine (VM), first, while the VM is still running, a copy-on-write (COW) disk file is created pointing to a parent disk file that the VM is using. Next, the VM is stopped, the VM's memory is marked COW, the device state of the VM is saved to memory, the VM is switched to use the COW disk file, and the VM begins running again for substantially the remainder of the checkpoint generation. Next, the device state that was stored in memory and the unmodified VM memory pages are saved to a checkpoint file. Also, a copy may be made of the parent disk file for retention as part of the checkpoint, or the original parent disk file may be retained as part of the checkpoint. If a copy of the parent disk file was made, then the COW disk file may be committed to the original parent disk file.

There are provided systems and methods for a fallback artificial intelligence (AI) system for redundancy during system failover. A service provider may provide AI systems for automated decision-making, such as for risk analysis, marketing, and the like. An AI system may operate in a production computing environment in order to provide AI decision-making based on input data, for example, by providing an output decision. In order to provide redundancy to the production AI system, the service provider may train a fallback AI system using the input/output data pairs from the production AI system. This may utilize a deep neural network and a continual learning trainer. Thereafter, when a failover condition is detected for the production AI system, the service provider may switch from the production AI system to the fallback AI system, which may provide decision-making operations during failure of within the production computing environment.

A method and system of checkpointing single process application groups and multi-process application groups. In an exemplary embodiment, the method may include creating at least one full checkpoint for each application in an application group, and creating at least one incremental application checkpoint for each application in the application group. Further, each of the at least one incremental application checkpoint may be automatically merged against a corresponding full application checkpoint. Further, checkpointing may be synchronized across all applications in the application group. In the exemplary embodiment, each application may use both fork( ) and exec( ) in any combination.

A fault-tolerant computer system architecture includes two types of operating domains: a conventional first domain (DID) that processes data and instructions, and a novel second domain (MM domain) which includes mentor processors for mentoring the DID according to “meta information” which includes but is not limited to data, algorithms and protective rule sets. The term “mentoring” (as defined herein below) refers to, among other things, applying and using meta information to enforce rule sets and/or dynamically erecting abstractions and virtualizations by which resources in the DID are shuffled around for, inter alia, efficiency and fault correction. Meta Mentor processors create systems and sub-systems by means of fault tolerant mentor switches that route signals to and from hardware and software entities. The systems and sub-systems created are distinct sub-architectures and unique configurations that may be operated as separately or concurrently as defined by the executing processes.

Techniques are disclosed relating to storage device failover. In one embodiment, a plurality of storage devices are represented as cluster resources to a cluster resource manager that manages cluster resources on a plurality of cluster nodes. An indication may be received that a failover operation is requested with respect to one of the plurality of storage devices. In response to the indication, the cluster resource manager may initiate the failover operation. In some embodiments, the failover operation includes changing a first access state of the storage device and a second access state of another storage device. In such an embodiment, the storage device and the other storage device may be associated with a logical unit number. In some embodiments, the storage device is located within a first of the plurality of cluster nodes; the other storage device is located within a second of the plurality of cluster nodes.

Some examples relate generally to a data management platform comprising a storage device configured to store secondary data and one or more processors in communication with the storage device and configured to perform certain operations. The operations may include identifying an aspect of the secondary data stored in the storage device, the secondary data including a backup of respective primary data stored in a primary data source; identifying or receiving an indication of a target to receive data associated with the identified aspect of the secondary data; and transmitting the data associated with the aspect of the secondary data to the target as a push transmission.

A system wherein a request can be processed both in a cloud service and autonomously or locally via a client, and a monitoring of the network quality, for example, the presence of delay, is carried out, and wherein, depending on the current accessibility of the cloud platform, optionally a local alternative calculation is carried out, which may be slower or, for example in the event of real-time requirements, is of a lower quality than the corresponding cloud service, and the result thereof is then temporarily used alternatively by the client is provided. In this way, cloud services can be locally buffered, thereby cushioning connection interruptions between end device and cloud platform, whereby cloud services can be used in a transparent manner for the user and, even in the event of connection interruptions.

A method for configuring replicas in a distributed computing system is disclosed. In one example embodiment, a plurality of replicas with associated bootstrap modules may be created. The same bootstrap module code may be used for each replica, thereby simplifying configuration. Using the bootstrap module, the replicas may automatically configure themselves and self-assign a role for a set of predetermined roles such as master and worker. The bootstrap module may check a predetermined location such as a shared network folder for earlier registration entries and then self-select based on the remaining available roles. The bootstrap module may also store its own registration entry to inform subsequent replicas of the role and network address for the current replica so that they may self-configure correctly.

Provided is a method for orchestrating a container-based application that is executed on a terminal device, in which implementation information is received in an orchestration slave unit on the terminal device via a communication connection from an orchestration master unit, and the application is configured and/or controlled by the orchestration slave unit based on the implementation information, wherein the received implementation information is additionally saved persistently in a memory unit in the terminal device, and if the communication connection to the orchestration master unit is interrupted, the most recently saved implementation information is retrieved from the orchestration slave unit and the application is configured and/or controlled based on the most recently saved implementation information.