Abstract: Storage resource usage amount(s) are determined. For instance, storage resource usage data in a historical period related to a user is acquired. The pattern information of the storage resource usage data is determined according to a time series of the storage resource usage data, the time series being a series of observed values of the storage resource usage data in the historical period. In addition, the storage resource usage amount for a target period of the user can be determined based on the pattern information and the storage resource usage data. The pattern information at least includes at least one of a trend pattern, a cycle pattern, or an irregular pattern. Beneficially, a storage resource usage amount of a user can be more accurately determined in a future period, thereby providing the user with valuable reference information.

Abstract: Methods, system, and non-transitory processor-readable storage medium for an Out of Memory test baseline system are provided herein. An example method includes executing a plurality of test cases on a system. A test score calculation module calculates a test case score for each of the executed test cases in a subset of the plurality of test cases. An Out of Memory (OOM) test baseline configuration system trains a machine learning system, using the subset test scores, to predict a baseline test score for an unexecuted test case. A test case score prediction module predicts the baseline test score for the unexecuted test case. A test case configuration tuning module tunes the unexecuted test case to determine a baseline configuration for the unexecuted test case, to identify OOM issues when the unexecuted test case is executed on a test system.

Abstract: 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.

Abstract: Provided is a method for preparing a display substrate. The method includes: providing a substrate, the substrate including a plurality of pixel island regions spaced apart and a plurality of bridge regions connecting adjacent pixel island regions; forming thin film transistors and first signal lines in the pixel island regions, and forming first connecting bridges in the bridge regions; and forming second signal lines, second connecting bridges, and a source/drain layer on the substrate by a one-time patterning process.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus comprises a processing device configured to identify test cases and test beds on which the test cases are configured to run, and to create test bed pools each associated with one of the test cases and comprising at least one of the test beds. The processing device is also configured to determine priority levels of the test cases and dependency degrees of the test beds. The processing device is further configured to generate a testing plan for testing a given product, the testing plan comprising a test case execution order for the test cases and a mapping of the test cases to the test beds, the test case execution order being determined based on the priority levels and the mapping of the test cases to the test beds being determined based on the dependency degrees. The processing device is further configured to execute the testing plan.

Abstract: An apparatus comprises a processing device configured to obtain information characterizing historical boot times for virtual machines associated with virtual volumes hosted on a storage cluster comprising a plurality of storage nodes, and to determine, based at least in part on the obtained information, whether any of the storage nodes has at least a threshold likelihood of experiencing a designated virtual machine boot condition during a given time period. The processing device is further configured, responsive to determining that a first one of the storage nodes has at least the threshold likelihood of experiencing the designated virtual machine boot condition during the given time period, to identify a subset of the virtual machines associated with a subset of the virtual volumes hosted on the first storage node and to move at least one of the subset of the virtual volumes to a second one of the storage nodes.

Abstract: Methods, system, and non-transitory processor-readable storage medium for a test case execution system are provided herein. An example method includes selecting an unexecuted test case from a plurality of test cases in a software testing lifecycle system. The test case execution system calculates a weighted metric associated with a tester for each tester in a plurality of testers. Using the weighted metric, the test case execution system determines a tester to which to assign the unexecuted test case for execution, where a tester is associated with a tester system, and assigns the unexecuted test case to the tester. The test case execution system receives results associated with executing the unexecuted test case on the tester system resulting in an executed test case. The test case execution system determines a test case execution value for the executed test case based on test failures associated with the executed test case.

Abstract: 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.

Abstract: Techniques reconstruct a storage system. A first extent access load and a second extent access load of a first malfunctioning extent and a second malfunctioning extent in a malfunctioning storage device in the storage system are acquired, respectively. The first malfunctioning extent is selected as a source extent in response to determining that the first extent access load is lower than the second extent access load. Among multiple idle extents in the storage system, a set of destination extents that can be used as a reconstruction destination of the source extent are generated. A destination extent is selected from the set of destination extents as the reconstruction destination of the source extent. It is possible to preferentially select a reconstruction destination for a malfunctioning extent with a low access load, thereby increasing the reconstruction speed of the storage system.

Abstract: Methods, system, and non-transitory processor-readable storage medium for feature coverage system are provided herein. An example method includes selecting a test case from a plurality of test cases in a software testing lifecycle system. A feature coverage system calculates a weighted metric associated with a test cycle for each test cycle in a plurality of test cycles, where each of the plurality of test cycles comprises a subset of the plurality of test cases. Using the weighted metric, the feature coverage system determines a test cycle in which to assign the test case to balance test feature coverage within the plurality of test cycles, and assigns the test case to the test cycle. The software testing lifecycle system then executes the test cycle on a system.

Abstract: Methods, apparatus, and processor-readable storage media for placement of virtual volumes based on activity levels are provided herein. An example computer-implemented method includes obtaining a request to create a new virtual volume on a storage cluster comprising a plurality of storage nodes; determining a total activity level for each of a plurality of storage nodes of the storage cluster in response to the request, wherein the total activity level for a given storage node is determined based at least in part on an activity level value of each virtual volume currently hosted on the storage node; selecting one of the plurality of storage nodes to host the new virtual volume based at least in part on the determined total activity levels; and creating the new virtual volume on the selected storage node.

Abstract: An apparatus comprises a processing device configured to extract, from edge computing nodes in an edge computing environment, sets of knowledge parameters characterizing information generated by additional processing devices associated with the edge computing nodes. The processing device is also configured to generate groups of the edge computing nodes utilizing at least one clustering algorithm that takes into account the sets of knowledge parameters extracted from the edge computing nodes. A given group of the edge computing nodes comprises at least two edge computing nodes having respective sets of extracted knowledge parameters exhibiting at least a threshold level of similarity with one another. The processing device is further configured to control operation of the at least two edge computing nodes in the given group based at least in part on sharing knowledge parameters in the sets of knowledge parameters extracted from the at least two edge computing nodes.

Abstract: Techniques manage an access load of a storage system. For storage devices in the storage system, access histories of the storage devices in a previous time period are received respectively. Access loads of the storage devices in a future time period are determined respectively based on the access histories of the storage devices, the future time period being between a current time point and a future time point for performing future load balancing. An access balance degree of the storage system at the future time point is acquired based on the access loads. Load balancing is performed among the storage devices in response to determining that the access balance degree satisfies a first predetermined condition. Accordingly, it is possible to determine an access load of each storage device in a storage system more accurately and ensure that the access loads of the storage devices are in a balanced state.

Abstract: 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.

Abstract: Storage devices in a pool are divided into at least one group with a first number of storage devices in an existing group not higher than a range. When a second number of storage devices are added to the resource pool, a sum of the first number and the second number is determined. A new group is created based on at least a portion of the second number of storage devices when the sum does not satisfy the range; and another portion of the second number of storage devices are added to the existing group. A first storage space portion in each of a set of shared storage devices selected from the existing group is allocated to the existing group, and a second storage space portion in each of the set of shared storage devices is allocated to the new group. The storage space utilization rate can be increased.

Abstract: Methods, system, and non-transitory processor-readable storage medium for test coverage optimizing system are provided herein. An example method includes executing at least one test case on a system. A weighted average baseline metrics calculation module calculates a weighted average baseline metric for at least one test case using test related failures that occurred during the execution. A coverage imbalance module calculates a coverage imbalance associated with a test domain using the weighted average baseline metric, where the test domain comprises the at least one test case.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for identifying a memory snapshot. The method includes determining, based on operation-related historical information for a plurality of memory snapshots, a probability that the plurality of memory snapshots are operated within a first time period. The method further includes, if the determined probability is less than a threshold associated with a duration of the first time period, identifying the plurality of memory snapshots for deletion. By use of the method, memory snapshots with a low probability of being operated in a future period of time can be identified, so that such memory snapshots are deleted to reduce usage of storage resources.

Abstract: 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.