Emc IP Holding Company LLC

Dell Products Inc.

Bing Liu

Tao Chen

Man Lv

Parmeshwr Prasad

Rahul Deo Vishwakarma

Parminder Singh Sethi

Lingdong Weng

Jehuda Shemer

Kfir Wolfson

Wei Lin

Yong Colin Zou

Jawad Said

Zheng Li

Martin Chaojun Mei

Assaf Natanzon

Geng Han

Shuguang Gong

Cheng Wang

James Lei Ni

Jian Gao

Yi Bin Wang

George Mathew

Cheng Fang Wang

Madhuri Dwarakanath

Jet Chen

Deepu M Sreedhar

Joel Miller

Xingyu Liu

Eric Yang Song

Long Wang

Ming Zhang

Yun Zhang

Lakshmi Saroja Nalam

Qi Chen

Colin Yong Zou

Qingxiao Zheng

Min Liu

Tao Xu

Joel Vance Miller

Jun Tang

Jitendra Singh

Vasanth Sathyanarayanan

Alice Cuicui Zhao

Lu Chen

Srinath Kappgal

Simin Wang

Daniel Savilonis

Fang You

Embodiments of the present disclosure relate to a model training method, a failure determining method, an electronic device, and a computer program product. The model training method includes: acquiring a plurality of disk failure data sets collected in a first time period; acquiring another disk failure data set that is collected at a predetermined time point after the first time period and indicates failure information of at least one failed sector set; and training a failure determining model based on the plurality of disk failure data sets and the failure information, so that a probability of matching of predicted failure information at a predetermined time point determined by the trained failure determining model based on the plurality of disk failure data sets and the failure information is greater than a first threshold probability. By using the technical solution of the present disclosure, it is possible to predict the failure information that will occur in the sector set included in a disk based on the disk failure data set associated with a failed sector, so that a user or administrator of the disk can know the failure condition that will occur in the sector set of the disk in advance.

Model training method, failure determining method, electronic device, and program product

A request is received to upgrade firmware of a set of disk array enclosures (DAEs) in a storage system. Each DAE is chained to another DAE by first and second chains. The first chain provides an active path from a host server to the DAEs. The second chain provides a secondary path from the host server to the DAEs. A determination is made as to whether the secondary path is operational. If the secondary path is operational, data of the active path is failed over to the secondary path, the active path now being an inactive path. Firmware along the inactive path is upgraded. If the secondary path is not operational, the request to upgrade firmware is denied.

Framework for upgrading firmware of disk array enclosures (DAES)

A request is received to upgrade firmware for disk array enclosures (DAEs). Each DAE is reachable by a host server using first and second paths. The first path extends from a first port of a first HBA of the host server to each of a set of first input/output modules (IOMs) in the DAEs. The first IOMs have first SAS expanders to which storage disks are connected. A flag is set in firmware of each first IOM that causes SMP and SES protocol services of a DAE to mark each respective storage disk connected to each respective SAS expander of the first SAS expanders as vacant. During the firmware upgrade, advertising to a SAS driver topology change events indicating that a storage disk has been removed or added is withheld based on the flag.

Disk array enclosure (DAE) firmware-based systems and methods for firmware upgrades

A method, a device, and a computer program product for resource scheduling is disclosed. The method includes determining a job initiated by a virtual machine. The job requests to invoke at least one virtual function in a set of virtual functions associated with the virtual machine and each virtual function in the set of virtual functions is configured to utilize an accelerator resource to provide a single type of acceleration service. The method further includes determining, based on a job type of the job, a first accelerator resource allocated to the at least one virtual function. The accelerator resources required by the virtual functions invoked by the job may then be guaranteed, improving the execution efficiency of the job.

Method, device and computer program product for resource scheduling

Data protection operations including replication operations are disclosed. Virtual machines, applications, and/or application data are replicated according to at least one strategy. The replication strategy can improve performance of the recovery operation.

Cost-optimized true zero recovery time objective for multiple applications

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for managing a storage system. The method includes: based on respective task types of a plurality of tasks to be executed, allocating the plurality of tasks to a plurality of accelerator resources in a storage system for processing; at least for a first accelerator resource in the plurality of accelerator resources, determining a first polling interval based on an average task size of a first group of tasks allocated to the first accelerator resource; and scheduling the execution of the first group of tasks at the first accelerator resource at the first polling interval. The embodiments of the present disclosure can optimize the scheduling of the tasks to be executed on the plurality of accelerator resources, thereby optimizing system performance.

Method, device, and computer program product for managing storage system

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for storage performance expansion. The method includes acquiring, from a client, backup settings of a user for backing up data in a storage system and the user's priority. The method further includes determining, based on the user's priority, an input/output (I/O) specification of the storage system that is able to be allocated to the client. The method further includes determining, based on the backup settings, a time period in which a peak value of an I/O load of the storage system occurs. The method further includes increasing I/O performance of the storage system in response to that the peak value is greater than an upper limit of the I/O specification. The embodiments of the present disclosure can solve the I/O peak problem more effectively without interrupting a data backup or restoration service.

Method, electronic device, and computer program product for storage performance expansion

In one aspect, an example methodology implementing the disclosed techniques includes, by a disk controller of a plurality of disks of a data storage system, collecting usage telemetry from the plurality of disks and initiating determination of one or more candidate time slots for performing a battery learn cycle using a machine learning model. The machine learning model is trained using the usage telemetry from the plurality of disks. The method also includes, by the disk controller, presenting one or more of the one or more candidate time slots for performing a battery learn cycle.

Battery learn cycle management

Techniques for intelligently routing IO to a storage class memory (SCM) namespace are disclosed. A configuration for a namespace is determined, where the configuration indicates a type of IO that the namespace is structured to handle. Details about the configuration of the namespace are stored in a repository. A forwarding rule is generated based on the namespace's stored configuration. When incoming IO having attributes similar to that type is received, implementation of the forwarding rule causes the incoming IO to be directed to the namespace. Attributes of a particular incoming IO are determined. As a result of the attributes satisfying a similarity threshold relative to the type, the forwarding rule is implemented such that the particular incoming IO is directed to the namespace.

Forwarding incoming IO to SCM namespaces

A method includes: generating a machine learning model based on historical operation data of the storage system, the storage system being used for backing up and restoring data in a client. The method further includes: determining, by the machine learning model, input/output (I/O) utilization of the storage system for a future time period. The method further includes: determining, in response to determining that the I/O utilization exceeds a predetermined threshold at a first time instance in the future, an I/O capacity corresponding to the determined I/O utilization based on the determined I/O utilization. The method further includes: adjusting the performance of the storage system based on the determined I/O capacity. The embodiments of the present disclosure enable more effective resolution of the I/O spike problem without interrupting data backup or restoration services.

Method, electronic device, and computer program product for adjusting performance of storage system

One example method includes accessing a dataset, selecting a list of attributes of the dataset, each of the attributes being selected based on a determination that the attribute is affecting growth of the dataset and affecting an amount of data storage space consumed by the dataset, assigning a SHAP score to each attribute, using the SHAP scores to assign respective weights to each attribute, deriving drift and shock information for the dataset, and based on the drift and shock information, calculating a risk score that a storage capacity of an asset where the dataset is stored will be exhausted within a particular time interval.

Stochastic risk scoring with counterfactual analysis for storage capacity

The present disclosure relates to a method, a device, and a program product for managing a computing resource in a storage system. In one method, a processing request for processing a task using a computing resource is received. A length of time required for processing the task is acquired based on a usage state of the computing resource. A workload of the computing resource for processing a future data access request for the storage system within a future time period is determined based on a load model of the computing resource and a current workload of the computing resource. The load model describes an association relationship between a previous load and a subsequent load of the computing resource for processing a historical data access request for the storage system. A target time period matching the length of time is selected from the future time period based on the workload for processing the task. A corresponding device and a corresponding computer program product are provided. Available computing resources in the storage system can be fully utilized. By choosing a target time period with a relatively low workload, a task can be processed in a more efficient manner.

Method, device, and program product for managing computing resource in storage system

The present disclosure relates to a method, a device, and a program product for managing multiple computing tasks on a batch basis. A method includes: identifying a task type of the multiple computing tasks in response to receiving a request to use a computing unit in a computing system to perform the multiple computing tasks; acquiring a scheduling time overhead incurred for scheduling the multiple computing tasks for execution by the computing unit; determining, based on the task type and the scheduling time overhead, a batch size for dividing the multiple computing tasks; and dividing the multiple computing tasks into at least one batch based on the batch size. A corresponding device and a corresponding computer program product are provided. With the example implementations of the present disclosure, the batch size for dividing multiple computing tasks can be dynamically determined, so that the performance of the computing system can meet user demands.

Method, device, and program product for managing multiple computing tasks based on batch

A method includes monitoring a first input/output (I/O) load set of the block storage system within a first time period and a second I/O load set within a second time period, wherein the block storage system is configured to back up and restore data in a client and has preconfigured predetermined thresholds. The method further includes determining a change of an I/O load of the block storage system based on the first I/O load set and the second I/O load set. The method further includes determining, based on the change of the I/O load, that an I/O load within a third time period reaches a first predetermined threshold. The method further includes adjusting I/O performance of the block storage system based on the I/O load within the third time period in response to determining that the I/O load within the third time period reaches the first predetermined threshold.

Method, electronic device, and computer program product for adjusting performance of block storage system

One example method includes analyzing an IO pattern comprising IOs issued by a client application, based on the analyzing, generating a rule relating to an NFIT, using the rule to modify the NFIT, and after the NFIT is modified, changing a configuration of SCM firmware based on the NFIT. Because the modified SCM firmware may have a configuration directly related to the IO pattern, the SCM may function relatively more efficiently and effectively for that IO pattern.

Based on IO pattern influence NFIT table for persistence for SCM firmware

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Tianjin, China

Bing Liu