International Business Machines Corporation

Sangeetha Seshadri

Lawrence Yiumchee Chiu

Paul Henri Muench

Aayush Gupta

Umesh Deshpande

Yang Liu

Hyojun Kim

Nicholas Linck

Yu-Cheng Hsu

Priyaranjan Behera

Yong Guo

Wilfred Edmund Plouffe, Jr

Mei Mei

Chao G Li

Sandeep R Patil

Roman Alexander Pletka

Ramani Ranjan Routray

Nikolas Ioannou

Ioannis Koltsidas

Stephen LaRoux Blinick

Maohua Lu

Mohit Saxena

Luis Angel D Bathen

Gabor Madl

Leo Shyh-Wei Luan

Thanh Tan Pham

Clement Lambert Dickey

David William Archer

Yang Zhou

Travis Janssen

Binayak Dutta

Yuquan Shan

Abdurrahman Yasar

Violet Rodriguez

Xioa-Yu Hu

Chetan Sharma

A computer-implemented method (CIM), according to one embodiment, includes simulating placement of a volume group on a first potential target storage system, and determining a first performance limit for the first potential target storage system based on the simulating. The method further includes predicting a duration the first potential target storage system can host the volume group without performance degradation, based on the first performance limit, and outputting a notification to a customer device, where the notification includes an indication of the predicted duration. A computer program product (CPP), according to another embodiment, includes a set of one or more computer-readable storage media, and program instructions, collectively stored in the set of one or more storage media, for causing a processor set to perform the foregoing method.

Performance-based migration for a volume group

A computer-implemented method, a computer system and a computer program product efficiently select restore points in a continuous data protection environment. The method includes receiving log entries that include restore points that correspond to data stored on nodes in the continuous data protection environment. The method also includes identifying an interesting restore point from the log entries. The method further includes grouping the interesting restore point for recovery based on one or more of a confidence score and a restore time. In addition, the method includes loading the group of interesting restore points on available nodes in the continuous data protection environment. The method also includes determining whether the data corresponding to each interesting restore point in the group is valid using a validation function. Lastly, the method includes discarding the interesting restore point when the data corresponding to the interesting restore point is not valid.

Efficient recovery in continuous data protection environments

Technology for identifying relevant data (also called 'data of interest') for applying a machine logic based rule and for increasing the frequency of making backups (for example, snapshots) of the relevant data.

Intelligent backup engine for data freshness

A computer-implemented method according to one embodiment includes creating a log of one or more events occurring during a performance of continuous data protection (CDP) for an instance of data; in response to identifying a conclusion of the performance of CDP for the instance of data, determining a value for the one or more events within the log; and conditionally creating and storing a snapshot of the instance of data, based on a comparison of the value for the one or more events to a predetermined threshold.

Performing conditional snapshot creation during continuous data protection age-out

A computer-implemented method according to one embodiment includes retrieving details of a data backup system; determining whether a first data backup operation will complete within the data backup system before a second data backup operation is initiated within the data backup system, based on the details of the data backup system; and conditionally adjusting one or more elements of the data backup system, based on the determining.

Adjusting data backups based on system details

A computer-implemented method according to one embodiment includes causing a failure event in each of a plurality of microservices of a system and collecting failure effect data associated with the caused failure events. A mapping is created detailing transition of the microservices between different states and the collected failure effect data is analyzed for creating the mapping. The method further includes outputting a predetermined notification in response to a determination that a first of the microservices is close to experiencing a predicted failure event, and outputting a suggested solution for repairing the system in response to a determination that the system has failed, using the mapping to identify a root cause of the system failure. Using the mapping to identify the root cause of the system failure includes identifying the microservices that caused the system failure.

Identification and/or prediction of failures in a microservice architecture for enabling automatically-repairing solutions

Embodiments are disclosed for a method. The method includes generating a backing layer having a file volume, in a layering file format and representing a portion of a volume snapshot that is backed up on a slower storage, and enabling an associated virtual machine. The method also includes generating an active layer comprising a second file volume, in the layering file format, representing a second portion of the volume snapshot, and enabling the associated virtual machine. The method further includes starting a process to pull a plurality of blocks from the backing layer to the active layer.

Cloning storage volumes from backup

One embodiment provides a method for recovery from failures during garbage collection processing in a system including recording, by a processor, a specific offset within a garbage collection target slot of a log structure associated with a garbage collection transaction. Each transaction record of the garbage collection transaction includes a garbage collection target slot, a victim slot and a beginning offset in the garbage collection target slot.

Chronologically ordered log-structured key-value store from failures during garbage collection

Provided are a computer program product, system, and method for managing data in a storage. Each index entry in and index identifies a log address in the log of one of the records. Each record in the log includes a key and value, and the log addresses map to physical addresses in the storage. A plurality of processed log addresses in the log are processed by determining whether the index indicates that the processed log address includes a current version of the record in the log; adding the record at the processed log address to a selected log address in the log in response to determining that the index indicates that the processed log address comprises the current version of the record; and deleting a record at the physical address in the storage to which the processed log address maps.

Managing data in storage according to a log structure

One embodiment provides a method for recovery after failure using a checkpoint in a chronological log-structured key-value store in a system including writing tombstone entries in a log structure for dirty checkpoint records to point to data records in an aborted target slot.

Fast recovery from failures in a chronologically ordered log-structured key-value storage system

Cognitive compression with varying structural granularities in a NoSQL database by establishing a data training set for compressing and decompressing data stored within the NoSQL database. The data training set includes received user policy goals, compression parameters, and metered feedback associated with data usage and workload characteristics. A compression parameter model is dynamically implemented in real-time for the data selected according to the established data training set to compress and decompress the data at a given structural granularity.

Cognitive compression with varying structural granularities in NoSQL databases

Embodiments for optimizing database backups to achieve a Recovery Time Object (RTO). A user-defined RTO configured for one or more databases is received. A backup frequency for initiating backups of the one or more databases is determined based on a continuously predicted recovery time associated with a plurality of factors. The backups of the one or more databases are executed at the determined backup frequency to ensure the user-defined RTO is achieved for the backups of the one or more databases. In some embodiments, a recovery window of the one or more databases may be increased using an RTO-aware tiered or remote storage caching operation for portions of the database, and an RTO-aware re-sharding operation on sharded databases may be performed when the backup frequency exceeds a predetermined threshold such that each shard may be restored within the user-defined RTO.

Selective placement and adaptive backups for point-in-time database recovery

In accordance with one aspect of the present description, in response to a detection by a storage controller, of an operation by a host relating to migration of input/output operations from one host to another, a cache server of a storage controller, transmits to a target cache client of the target host, a cache map of the source cache of the source host wherein the cache map identifies locations of a portion of the storage cached in the source cache. In response, the cache client of the target host, may populate the target cache of the target host with data from the locations of the portion of the storage, as identified by the cache map transmitted by the cache server, which may reduce cache warming time. Other features or advantages may be realized in addition to or instead of those described herein, depending upon the particular application.

Cache migration management in a virtualized distributed computing system

One embodiment provides a method including storing a key-value store in a memory. Out-of-place operations are performed to maintain chronological ordering of the operations by enforcing ordering of the operations on a storage layer in presence of a garbage collection transaction without an explicit secondary index.

Chronologically ordered out-of-place update key-value storage system

A computer system may include a storage device and nodes cooperating therewith to define a key value database architecture. A first node of the nodes may be configured to generate a plurality of checkpoints. The computer system may also include a second node of the nodes configured to operate a log feed cache manager based upon the checkpoints generated by the first node.

Growing community of inventors

San Jose, CA, United States of America

Sangeetha Seshadri