Determining remaining hardware life in a storage system, including: receiving data about a plurality of hardware components including data describing the usage of each hardware component and the state of each hardware component; analyzing the data to determine a remaining hardware life for each hardware component in a group of components; and distributing workloads in order to balance wear amongst the hardware components in the group.

A method and system for verifying state monitor reliability in hyper-converged infrastructure (HCI) appliances. Specifically, the method and system disclosed herein entail using a supervised machine learning model—i.e., a classification decision tree—to accurately distinguish whether conflicting event notifications, logged across multiple state monitors tracking state on an HCI appliance, are directed to a real event or a non-real event. The classification decision tree, generated based at least on information gains calculated for the multiple state monitors, may reflect which state monitor(s) is/are more reliable in accurately classifying the conflicting event notifications.

A system and method for determining automated uptime of internet-based software may include one or more memories storing check suites, each check suite including an ordered sequence of a plurality of checks corresponding to an execution flow of an ordered sequence of functions of the internet-based software; and one or more processors configured to determine a total amount of down time for a monitoring period by: during each time interval of the monitoring period, performing each of the plurality of check suites on the internet-based software, and applying at least one weighing factor to results of performing each of the plurality of check suites.

Techniques for storage management involve: determining expected usage time of a first storage disk in a set of storage disks, at least a part of the set of storage disks being configured to form at least one redundant array of independent disks (RAID); moving data in a first storage block, associated with a first RAID in the at least one RAID, in the first storage disk to a second storage block in a second storage disk in the set of storage disks based on a determination that the expected usage time is less than a predetermined threshold value; and replacing the first storage block with the second storage block, so that the second storage block is associated with the first RAID. Based on this manner, the stability of a storage system can be improved.

Provided is a method for analyzing and designing a physical system architecture of a safety-critical system, wherein a physical system analysis model representing the physical system architecture of the safety-critical system is modified incrementally until calculated failure rates of failure modes of the physical system analysis model are less or equal to failure rates of corresponding failure modes of a functional system analysis model representing a functional system architecture of the safety-critical system.

An asset management method for a substation in accordance with the present invention generates a unique reliability model for each element of the substation by comparing reliability of a reference reliability model for each substation type with a health index of the each element thereof generated based on state data and real-time monitoring data of the each element of the substation and compensating the reference reliability model for the each element of the substation; assessing system reliability index and economic feasibility for each maintenance scenario based on a reference system reliability model for each candidate element subject to maintenance among the elements of the substation; selecting a maintenance scenario as a result of the health index and the unique reliability model for the each element of the substation, the system reliability index, and the economic feasibility and updating the unique reliability model for the each element of the substation by executing maintenance.

A method of operating a storage device includes sensing a standby current flowing through the storage device, determining based on the sensed standby current and at least one reference value whether a product abnormality has occurred within the storage device, and when it is determined the product abnormality has occurred, performing a step-wise control operation in which two or more control processes associated with an operation of the storage device are sequentially executed.

Methods for backup and recovery are disclosed. The method includes determining, based on attributes of at least one of one or more files included in data to be backed up, priorities of data blocks associated with the at least one file and storing the data to be backed up and indications of the determined priorities of the data blocks to a second storage device. The methods may determine data blocks that are more important for recovery while backing up data, so that backup data can be recovered faster in future.

A probability calculation result database includes a combination countermeasure table having an error code group including an error code of a repair target apparatus and a plurality error code indicating the relevant error code appeared in the past, as well as a countermeasure content against the error code and the plurality error code, and probabilities that the countermeasure content against both the error code and the plurality error code are taken respectively; a data processing unit which generates a combination table containing a new error code and a new plurality error code from apparatus error information obtained from the repair target apparatus, and repair prediction processing for predicting recommendable countermeasure contents against the new error code and plurality error code on the basis of the combination table and the probability calculation result database; and a result processing unit for providing the results of the repair prediction processing in order.

An embodiment of the invention may include a method, computer program product and computer system for forecasting future states of a multi-active cloud. The method, computer program product and computer system may include a computing device that determines the operating state of the passive server. The operating state of the passive server is one or more of a deploying state and a smoke testing state. The computing device may determine the probability of the operating state successfully completing. The computing device may delay the second asynchronous workflow until the operating state successfully completes based on the probability of the operating state successfully completing exceeding a predetermined value.