Dell Products Inc.

Emc IP Holding Company LLC

Hailan Dong

Chi Chen

Huijuan Fan

Changyue Dai

Xu Chen

Changxu Jiang

Peilei Chen

Zhenzong Jiang

Si Zhang

En Shi

Fanliang Lin

An apparatus comprises a processing device configured to maintain, for a storage system implementing a mapped redundant array of independent disks (RAID) configuration including disk groups providing RAID groups comprising data and hot spare extents, a shared pool of hot spare extents comprising first and second sets of hot spare extents from first and second ones of the disk groups. The processing device is further configured to detect failure of a disk in the first disk group, to determine whether available ones of the first set of hot spare extents provide sufficient storage capacity for rebuilding the failed disk and, responsive to determining that available ones of the first set of hot spare extents do not provide sufficient storage capacity for rebuilding the failed disk, to rebuild the failed disk utilizing one or more of the second set of hot spare extents in the shared pool of hot spare extents.

Mapped raid configuration with multiple disk groups and a shared pool of hot spare extents

A method includes: determining, in response to a failure of a disk where a RAID extent (RE) in a storage system is located, a first disk set based on a predetermined range and the failed disk, wherein the first disk set comprises a plurality of disks within the predetermined range that are associated with the RE; determining a second disk set for rebuilding data located on the failed disk based on other disks in the first disk set than the plurality of disks that are associated with the RE; determining scores for disks in the second disk set based on disk correlations, RE spans, and weights of the plurality of disks in the second disk set; determining a target disk among the plurality of disks in the second disk set based on the plurality of scores; and rebuilding the data located on the failed disk on the target disk.

Method, electronic device, and computer program product for data rebuilding

Embodiments of the present disclosure relate to a method, a device, and a computer program product for storing data. The method includes determining multiple access frequencies and multiple input/output (IO) modes of multiple data blocks, where the multiple data blocks are stored in a disk, and an IO mode in the multiple IO modes indicates an access size, a read operation proportion, and a sequential access proportion. The method further includes determining multiple cache hit ratios corresponding to the multiple IO modes. The method further includes determining multiple scores of the multiple data blocks based on the multiple access frequencies and the multiple cache hit ratios. In addition, the method further includes determining, based on the multiple scores of the multiple data blocks, a data block in the multiple data blocks to be replicated to a cache.

Method, device, and computer program product for storing data based on multiple cache hit ratios and associated prediction hit ratios

Techniques for storage testing involve: acquiring a first state of a storage system including first input/output (IO) load information; taking a first action based on the first state, the first action causing the first IO load information to be changed to second IO load information; updating the first action to be a reserved action for the first state if it is obtained based on the second IO load information that the storage system reaches a preset condition; and obtaining an action combination of a plurality of IO load information changes based on a plurality of reserved actions corresponding to a plurality of states, wherein the plurality of states include the first state. Accordingly, the most effective load combination change mode for the storage system can be found automatically and more accurately, so as to find more vulnerabilities of the storage system, thereby improving the efficiency of storage system testing.

Method and electronic device for storage testing

An apparatus comprises a processing device configured to generate a predicted data access frequency trend pattern of a storage object for a designated period of time, the storage object being stored in a first storage tier in a clustered storage system. The processing device is also configured to classify the storage object based on the predicted data access frequency trend pattern, and to determine a given storage tier in the clustered storage system to utilize for storage of the storage object during the designated period of time based on the predicted data access frequency trend pattern. The processing device is also configured, responsive to the given storage tier being different than the first storage tier, to select a type of data movement based on the classification of the storage object and to utilize the selected type of data movement to move the storage object to the given storage tier.

Data movement between storage tiers of a clustered storage system based on predicted data access frequency trend patterns

An apparatus comprises a processing device configured to receive a request to store data on a storage system, and to determine storage node correlation metrics for storage nodes of the storage system characterizing probabilities of input/output operations being directed to filesystems having data stored on the storage nodes. The processing device is also configured to select, based on the storage node correlation metrics, one of the storage nodes to utilize for storing a given portion of the data, and to determine storage device correlation metrics for storage devices of the selected storage node characterizing probabilities of input/output operations being directed to filesystems having data stored on the storage devices. The processing device is further configured to select, based on the storage device correlation metrics, one of the storage devices of the selected storage node to utilize for storing the given data portion.

Data placement selection among storage devices associated with storage nodes of a storage system

An apparatus comprises a processing device configured to determine, based on monitoring input/output (IO) operations, an IO activity level and a given IO pattern of a given storage object for a designated period of time, the given storage object being stored on a first storage tier of a storage system. The processing device is also configured to determine a write penalty associated with writing storage objects having the given IO pattern to a second storage tier of the storage system. The processing device is further configured to determine, based on the IO activity level of the given storage object and the determined write penalty, whether to move the given storage object from the first to the second storage tier and to move, responsive to an affirmative movement determination, the given storage object to the second storage tier.

Data movement based on write penalties of input/output patterns associated with storage objects

Techniques for managing disks involve determining performance information of an access pattern of a disk slice based on differences in performance parameters of the access pattern of the disk slice on a plurality of disks. Such techniques further involve determining a score for the disk slice based on the performance information and access frequency information of the disk slice. Such techniques further involve determining a position of the disk slice in the plurality of disks based on the score.

Method, electronic device and computer program product for managing disks based on performance information of access pattern

An apparatus comprises a processing device configured to identify storage workloads to be run on a storage system, and to determine a mix of input/output (TO) patterns associated with the identified storage workloads, the mix of IO patterns comprising a first set of IO patterns characterizing types of IO operations performed by a first storage workload and at least a second set of IO patterns characterizing types of IO operations performed by a second storage workload. The processing device is also configured to calculate an affinity metric for the mix of IO patterns, the calculated affinity metric characterizing a difference between (i) performance metrics for the mix of IO patterns running concurrently and (ii) the first and second sets of IO patterns running individually. The processing device is further configured to allocate the identified storage workloads to storage devices of the storage system based on the calculated affinity metric.

Storage workload allocation based on input/output pattern affinity calculations

An apparatus comprises a processing device configured to classify an input/output pattern of a given storage object that is stored utilizing a first type of storage resources associated with a first storage tier in the clustered storage system, and to identify, for the classified input/output pattern, a performance difference between utilizing first and second types of storage resources associated with first and second storage tiers in the clustered storage system. The processing device is further configured to determine, based at least in part on the identified performance difference, whether to move the given storage object from the first type of storage resources associated with the first storage tier to the second type of storage resources associated with the second storage tier in the clustered storage system and to move, responsive to the determination, the given storage object to the second type of storage resources associated with the second storage tier.

Data movement between storage tiers of a clustered storage system based on input/output patterns of storage objects

An apparatus comprises a processing device configured to obtain information associated with services hosted in an information technology infrastructure comprising a core data center hosting a first subset of the services and edge computing sites hosting a second subset of the services. The processing device is also configured to determine, based on the obtained information, values associated with parameters characterizing suitability of hosting respective ones of the services at the computing sites, and to generate, based on the determined values, scores for each of the services. The processing device is further configured to identify, based on the generated scores, at least a given one of the services to be migrated from the core data center to the edge computing devices or from the edge computing sites to the core data center. The processing device is further configured to migrate the given one of the services.

Automated service tiering between edge computing sites and a core data center

An apparatus comprises a processing device configured to monitor input/output (IO) operations for storage objects stored on storage devices of a storage system for a designated period of time, to identify IO patterns associated with the storage objects, wherein the IO patterns are associated with different wear level impacts on the storage devices of the storage system, and to determine a wear status of each storage device of the storage system. The processing device is also configured to select one or more storage objects to move from a first to a second storage device of the storage system based at least in part on the monitored input/output operations, the identified IO patterns, and the determined wear status of each storage device. The processing device is further configured to move the selected storage objects from the first to the second storage device to perform wear level balancing for the storage system.

Wear leveling between storage devices of a storage system based on input/output patterns of storage objects

An apparatus comprises at least one processing device that includes a processor coupled to a memory. The at least one processing device is configured to determine a current state of a storage system, the current state of the storage system comprising two or more input-output (IO) performance metric values for the storage system, to generate, utilizing a reinforcement learning framework, an IO throttling recommendation for the storage system based at least in part on the current state of the storage system, to apply the IO throttling recommendation to the storage system, and to update the reinforcement learning framework based at least in part on a subsequent state of the storage system following the application of the IO throttling recommendation to the storage system.

Storage system IO throttling utilizing a reinforcement learning framework

An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to obtain usage information for each of two or more storage systems of a storage cluster, and to determine a wear level of each of the storage systems of the storage cluster based at least in part on the obtained usage information. The at least one processing device is also configured to identify a wear level imbalance of the storage cluster based at least in part on the determined wear levels of each of the storage systems of the storage cluster. The at least one processing device is further configured, responsive to the identified wear level imbalance of the storage cluster being greater than an imbalance threshold, to move storage objects between the storage systems of the storage cluster.

Performing wear leveling between storage systems of a storage cluster

An apparatus comprises a processing device configured to initiate load balancing for a storage cluster comprising two or more storage nodes, to predict performance metrics for the storage nodes at two or more time points in a designated future period of time, and to select, based at least in part on the predicted performance metrics, a first one of the storage nodes as a source storage node and a second one of the storage nodes as a target storage node. The processing device is further configured to determine at least one storage object residing on the source storage node which, when migrated to the target storage node, reduces a performance imbalance rate of the storage cluster for at least the designated future period of time, and to perform load balancing for the storage cluster by migrating the at least one storage object from the source to the target storage node.

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Chengdu, China

Hailan Dong