Dell Products Inc.

Emc IP Holding Company LLC

Weilan Pu

Chi Chen

Jian Kang

Jianfei Yang

Mingyi Luo

Ruiyang Zhang

Srinivasa Raju Chamarthy

Jingyi Wang

Yang Zhang

Chaojun Zhao

Shuangshuang Liang

Si Zhang

Fang Du

Peilei Chen

Jie Wang

Wen Chen

Lifeng Zheng

Methods, system, and non-transitory processor-readable storage medium for a test selection system are provided herein. An example method includes selecting, by a test selection system, a regression test case from a plurality of regression test cases in a software testing lifecycle system. The test selection system determines a product release weight associated with the regression test case, where the product release weight incorporates new features associated with a product release. The test selection system obtains a regression test case value for the regression test case by applying the product release weight. The test selection system selects the regression test case for use in regression testing for the product release, based on the regression test case value and executes the regression test case on a system.

Intelligent way to select regression test cases in system test

Embodiments of multi-host link aggregation are described for active-active cluster implementations. In one or more embodiments, a multi-host LAG bond in the cluster is created with a bond MAC address designated for the bond. Afterwards, a virtual IP address may be created for all hosts within the multi-host LAG bond. Compared with existing active-active cluster implementation, the presented multi-host link aggregation embodiments do not require extra entities, e.g., load balancer or DNS, which could be a failure point or performance bottleneck, thus makes the active-active cluster implementations more efficient and robust. With the Multi-Host LACP bond and virtual IP on all hosts, the cluster may have increased bandwidth to the cluster service and utilize computing capacity in a network switch for improved workload distribution.

Multi-host link aggregation for active-active cluster

An information handling system acquires data chunks for a duration of at least one time slice, determines an overwrite frequency for the duration of the time slice of each of the data chunks, clusters the data chunks according to the overwrite frequency, and determines an overwrite frequency label for each cluster of the data chunks. The system may also determine a read frequency for the duration of the time slice of each of the data chunks, cluster the data chunks based on the read frequency, and determine a read frequency label for each of the cluster of the data chunks. The system may also construct a sorted tree based on the overwrite frequency label, the read frequency label, and a virtual logical block address of each of the data chunks.

Update-correlations based overwrite mechanism

An apparatus comprises at least one processing device configured to receive, by at least one network switch, a communication comprising a virtual logical device tag. The processing device is also configured, responsive to determining that the virtual logical device tag is a host-specific virtual logical device tag associated with a given host-specific virtual logical device of a given host device that is part of a multi-host link aggregation bond, to direct the communication to a given link for the given host device. The processing device is further configured, responsive to determining that the virtual logical device tag is a service-generic virtual logical device tag for a service provided by host devices which are part of the multi-host link aggregation bond) collectively, to select one of the host devices in accordance with a distribution algorithm and to direct the communication to a selected link for the selected host device.

Managing communications for host devices which are part of a multi-host link aggregation group

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for scheduling data collection. The method includes acquiring a plurality of running parameters of a storage system. The method further includes determining a plurality of grade ranges of each of the plurality of running parameters, the grade ranges indicating degrees of impact on scheduling for data collection. The method further includes determining a plurality of relevancies of the plurality of running parameters for the plurality of grade ranges. The method further includes determining scheduling for the data collection based on the plurality of relevancies. The method can dynamically determine when to perform data collection, thus avoiding data loss.

Method, electronic device, and computer program product for scheduling data collection

Techniques for managing data patterns involve: acquiring multiple sets of data patterns respectively associated with multiple collection devices, wherein a set of data patterns in the multiple sets of data patterns represent patterns of duplicate data in data from one of the multiple collection devices; dividing the multiple collection devices into multiple groups based on clusters of the multiple sets of data patterns; and determining, based on sets of data patterns associated with collection devices in a group in the multiple groups, a set of shared data patterns for sharing among the collection devices in the group. Accordingly, data patterns that can be shared among multiple collection devices can be determined in a more accurate and effective manner, thereby facilitating the removal of duplicate data from the multiple collection devices.

Method, device, and program product for managing data pattern

An apparatus comprises at least one processing device configured to collect, from a plurality of storage systems, data patterns for data stored in the plurality of storage systems and to cluster the plurality of storage systems into one or more data pattern sharing clusters based at least in part on the collected data patterns, a given one of the one or more data pattern sharing clusters comprising two or more of the plurality of storage systems. The at least one processing device is also configured to identify, for the given data pattern sharing cluster, a subset of the collected data patterns and to provide, to the two or more storage systems of the given data pattern sharing cluster, the identified subset of the data patterns, wherein the identified subset of the collected data patterns are utilized by the two or more storage systems in performing data deduplication.

Clustering storage systems for sharing of data patterns used for deduplication

A data-relationship-based FAST cache system includes a storage controller that is coupled to first storage device(s) and second storage device(s). The storage controller identifies a relationship between first data stored in the first storage device(s) and second data stored in the first storage device (s), with the relationship based on a difference between a first number of accesses of the first data associated with a first time period and a second number of accesses of the second data associated with the first time period being within an access difference threshold range. Subsequent to identifying the relationship, the storage controller determines that the first data has been accessed in the first storage device(s) a number of times within a second time period that exceeds a FAST cache threshold and, in response, moves both the first data and the second data to the second storage device(s) based on the relationship.

Data-relationship-based fast cache system

Techniques for data relocation involve: determining whether access frequency of a data block in a multi-tier storage system is periodic; in response to determining that the access frequency of the data block is periodic, determining a change cycle of the access frequency of the data block; and determining, based on the change cycle of the access frequency of the data block, priority of relocating the data block in the multi-tier storage system.

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Chengdu, China

Weilan Pu