Dell Products Inc.

Emc IP Holding Company LLC

Southwest Petroleum University

Chengdu Leps Technology Co., Ltd

Chi Chen

Huijuan Fan

Hailan Dong

Jianchun Guo

Changyue Dai

Cong Lu

Nan Wang

Weilan Pu

Jing Ye

Kun Wang

Zhihong Zhao

Bo Gou

Jie Lai

Ye Zhong

Jian Kang

Congbin Yin

Ruiyang Zhang

Yuxuan Liu

Jichuan Ren

Meiping Li

Xiao Li

En Shi

Yunchuan Zheng

Shouxin Wang

Yang Wu

Tao Zhang

Yanjun Liu

Yong Ren

Shan Ren

Bo Yang

Si Zhang

Jianfei Yang

Zhiqiang Hu

Chao Liu

Shaobin Zhang

Xianjun He

Su Diao

Qianli Lu

Qingsong Guo

Yuhang Zhao

Li Ma

Ming Li

Jiapeng Zhang

Hao Yang

Chaojun Tang

Mingyuan Zhou

Xinghao Gou

Youjing Duan

Xiang Bai

Jiandong Wang

Dilin Wen

Junfeng Li

Mingzhong Chen

Chuhao Huang

Liang Tao

Fenglan Huang

Pengcheng Tang

Wen Chen

Fanliang Lin

Yang Luo

Jiyu Wang

Bin Qian

Yingxian Ma

Xin Lei

Yongjun Xiao

Techniques for generating a test case involve: acquiring a first set of coding sequences representing a first set of test cases selected from a test case set for product testing. A test element in a test case of the test case set is coded based on the position of the test element in the element hierarchy of the test case set. The techniques further involve: generating a second set of coding sequences by performing a random variation related to at least one test element with respect to the first set of coding sequences; and generating a second set of test cases based on the second set of coding sequences. Accordingly, product testing and development may be automated, and the case set may change dynamically in a way that is adapted to product development while significantly reducing the overhead of designing and reviewing test cases throughout the product life cycle.

Method, device, and computer program product for generating test case

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for backing up data. The method may include acquiring a system state of a storage system according to a data backup task. The method further includes generating, by a reinforcement learning model, a plurality of candidate backup policies based on the system state. The method further includes determining a plurality of reward values for the plurality of candidate backup policies based on amounts of resources occupied by an IO task in a plurality of time periods corresponding to the data backup task. The method further includes selecting a target backup policy based on the plurality of reward values. The method further includes performing the data backup task using the target backup policy.

Method, electronic device, and computer program product for backing up data

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

An apparatus comprises a processing device configured to determine a workload level of each storage resource in a set of two or more storage resources, the workload levels being based at least in part on a processor performance metric, a memory performance metric, and a load performance metric. The processing device is also configured to identify a performance imbalance rate for the set of two or more storage resources, and to perform workload balancing for the set of two or more storage resources responsive to (i) the performance imbalance rate for the set of two or more storage resources exceeding a designated imbalance rate threshold and (ii) at least one storage resource in the set of two or more storage resources having a workload level exceeding a designated threshold workload level.

Multiple metric-based workload balancing between storage resources

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

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for testing. The method includes: obtaining code modification information, program error information, and test case information. The method further includes: selecting a first test case set associated with code modification records from the test case information according to the program error information. The method further includes: sorting multiple test cases in the first test case set to generate a test strategy for the code modification records. Embodiments of the present disclosure may select the best test case for current code fix to meet different test requirements and reduce the test time.

Method, device, and computer program product for testing

An apparatus comprises a processing device configured to detect a request for an updated snapshot schedule for an information technology asset, and to determine a current state of the information technology asset comprising a set of snapshot parameters of a current snapshot schedule and one or more performance metric values. The processing device is also configured to generate, utilizing a reinforcement learning framework, an updated parameter value for at least one of the snapshot parameters based at least in part on the current state. The processing device is further configured to monitor performance of the information technology asset utilizing the updated snapshot schedule comprising the updated parameter value for the at least one snapshot parameter, and to update the reinforcement learning framework based at least in part on a subsequent state of the information technology asset determined while monitoring performance of the information technology asset utilizing the updated snapshot schedule.

Generating parameter values for snapshot schedules utilizing a reinforcement learning framework

The present invention discloses a novel high-temperature visual acid etching test device and method; the test device includes a visual rock slab holder, an acid solution storage tank I, an acid solution storage tank II, a heating apparatus, a high-pressure water pump, a low-pressure acid pump, a back-pressure valve, an acid valve A, an acid valve B, an acid valve C, an acid valve D, an acid valve E, an acid valve F, a water valve G, a water valve H, a water valve I, a water valve J, and a water valve K; both the acid solution storage tank I and the acid solution storage tank II are provided with a piston therein, where the acid solution storage tank is divided into upper and lower independent parts by the piston. According to the present invention, the flowing process of an acid solution in a fracture can be directly observed to accurately characterize the distribution of the acid solution in the fracture, which facilitates a study on an acid etching mechanism. According to the present invention, an acid solution injection system is separated from a water injection system, so that only water is injected into the system through a high-pressure pump, effectively solving safety problems of acid solution pumping. In the test process of the present invention, a totally-enclosed acid solution system can ensure that the acid solution does not overflow at high temperature and high pressure, thus greatly increasing the safety of the test.

High-temperature visual acid etching experimental device and method

An apparatus comprises a processing device configured to receive, at a given node of a distributed file system cluster, a request to store one or more portions of data and to monitor performance parameters for each storage device associated with the given node, the performance parameters comprising a first performance parameter characterizing data access frequency and at least a second performance parameter characterizing available space. The processing device is also configured to determine an overall performance metric for each of the storage devices associated with the given node based at least in part on the monitored performance parameters and to select, based at least in part on the overall performance metrics, at least one of the storage devices associated with the given node on which to store the one or more portions of data.

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

A method includes: acquiring a plurality of historical routes of movement of a plurality of historical users in a geographic area, wherein each historical route includes at least a portion of a plurality of locations; determining, based on the plurality of historical routes and a plurality of current positions of a plurality of users in the geographic area, a set of predicted locations among the plurality of locations that the plurality of users will visit in the future, respectively, wherein the plurality of users use a tour guiding service associated with the plurality of locations that is provided by a mobile network; selecting a set of popular locations from the set of predicted locations based on the number of users among the plurality of users who will visit each predicted location in the set of predicted locations; and scheduling tour guiding resources associated with the set of popular locations.

Method, device, and computer program product for scheduling tour guiding resources

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

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Chengdu, China

Chi Chen