Emc Corporation

Emc IP Holding Company LLC

Dell Products Inc.

Ron Arnan

Sachin More

Adnan Sahin

Hui Wang

William Z Zahavi

Preston F Crow

Lee W Sapiro

Saymai Thammavong

Amnon Naamad

Tao Kai Lam

Ruben Michel

David W DesRoches

Yechiel Yochai

Victoria Dubrovsky

Hagay Dagan

Arieh Don

Adi Ofer

Thomas Earl Linnell

Erik P Smith

Eitan Bachmat

Paulo Abelha Ferreira

Pablo Nascimento Da Silva

Adriana Bechara Prado

Gilad Sade

Dennis F Flanagan

David J Allen

Fatemeh Azmandian

David P Moloney

Yedidia Atzmony

Hagit Brit-Artzi

Gil Ratsaby

Hagit Bachmat

Orit Levin Michael

Ian Adams

Described are techniques for modeling processing performed in a data storage system. Inputs received may include a plurality of workloads each denoting a workload for one of a plurality of storage groups, a plurality of service level objectives each denoting a target level of performance for one of the plurality of storage groups, a plurality of capacities each denoting a storage capacity of one of a plurality of storage tiers, and a plurality of maximum workloads each denoting a maximum workload capability of one of the plurality of storage tiers. Using the inputs, placement of data of the plurality of storage groups on the plurality of storage tiers may be modeled. Output(s) may be generated based on the modeling where the output(s) may include an amount of each of the plurality of storage tiers allocated by modeling to each of the plurality of storage groups.

Data storage system modeling using application service level objectives and specified workload limits for storage tiers

An apparatus comprises a host device configured to communicate over a network with a storage system. The host device comprises a plurality of host bus adaptors, and a multi-path input-output driver configured to control delivery of input-output operations from the host device to the storage system over selected ones of a plurality of paths through the network. The paths are associated with respective initiator-target pairs wherein each of the initiators comprises a corresponding one of the host bus adaptors and each of the targets comprises a corresponding one of a plurality of ports of the storage system. The host device monitors performance of the ports in processing input-output operations delivered thereto, detects an initiator-related condition based at least in part on the monitored performance, and automatically adjusts an assignment of one or more of the initiators to one or more of the targets based at least in part on the detected initiator-related condition.

Host device with multi-path layer configured for detection and resolution of initiator-related conditions

Telemetry data gathered from active deployed SAN nodes is used to create a machine learning model that predicts storage system performance, e.g. in terms of response time. The telemetry data may be filtered to remove outlier values and less relevant information before creating the training dataset. Engineered features may be created that include types of data that are not present in the telemetry data. For example, data types from the telemetry data may be combined to create engineered features that are more relevant than the individual data types. The engineered features are included in the training dataset. The machine learning model may be used to test possible configurations for a planned SAN node based on expected workload and performance requirements. Outputted data may include satisfactory configurations for a planned storage system.

Using telemetry data from different storage systems to predict response time

A complex object generator is implemented, for example, as an integrated development environment. The complex object generator includes a hierarchical object relationship data structure describing classes of objects, relationships between the classes of objects, and metrics associated with the classes of objects. The hierarchical object relationship data structure is parsed by parser to create a hierarchy of Java classes. A user interface uses the hierarchy of Java classes to constrain selection of objects and metrics during creation of the complex object. As input is received relative to selected objects and metrics, the complex object is incrementally built. By constraining object and metric selection using the hierarchy of Java classes, the complex object is guaranteed to be valid when built, thus reducing or eliminating the number of errors associated with building complex objects.

Method and apparatus for hierarchical generation of a complex object

Described is storage system and method for reducing power consumption. The storage system has first and second physical disks configured to provide mirroring. The first physical disk is placed into a power-saving mode of operation, while the second physical disk is in an active mode of operation responding to read and write requests. The first physical disk transitions from the power-saving mode of operation to an active mode of operation for destaging writes pending from cache to the first physical disk, while the second physical disk responds to read and write requests. The second physical disk transitions from the active mode of operation to the power-saving mode of operation, while the first physical disk responds to read and write requests.

Power saving mechanisms for a dynamic mirror service policy

For each storage array in a storage system, a single value score indicative of the overall health of the respective storage array is calculated. Storage array health score may be tracked over time and used to identify storage arrays in need of maintenance. The storage array health score may be calculated as a composite of four component scores: physical component health, logical component health, Service Level Objective compliance and Best Practice Configuration Compliance. The physical component and logical component scores may be based on multiple different category health scores, and each category health score may be based on multiple instance health scores. Health scores may be used to identify remedial actions and predict health score increase as a result of remedial actions.

Storage array health score

Power-saving mechanisms for a dynamic mirror service policy

A data management and archive method and apparatus, for implementation in an automated system to monitor and manage status, performance and configuration data for networked storage components. Analysis and cross-correlation of data related to the storage components can be done individually, collectively and/or comparatively. A collection manager component of a workload analyzer is implemented to start and stop data collection. The collection manager includes a command and control module that coordinates requests of data from at least one collection agent configured on at least one host connected to the storage component(s). The collection manager manages collection of data and effects file transfer of collected data according to a user specified policy, and maintains status of the data collected. The user specified policy allows the user to specify data collection 'shifts' (i.e. periodicity).

Method and apparatus for managing and archiving performance information relating to storage system

A data management and archive method and apparatus, such as for implementation in an automated system to monitor and manage status, performance and configuration data for a plurality of networked storage components. Analysis and cross-correlation of data related to the plurality of storage components can be done individually, collectively and/or comparatively. A collection manager component of a workload analyzer is implemented to start and stop data collection in the context of a system comprising at least one storage component (or at least two networked storage components). The collection manager includes a command and control module that coordinates requests of data from at least one collection agent configured on at least one host connected to the storage component(s). The collection manager manages collection of data and effects file transfer of collected data according to a user specified policy, and maintains status of the data collected. The user specified policy allows the user to specify data collection 'shifts' (i.e. periodicity).

Described is a storage system and method for managing and optimizing power consumption in a storage system. Logical devices are mapped to physical disk drives. A plurality of power profiles is defined. Each power profile is associated with a mode of operation for a physical disk drive. One of the power profiles is assigned to each logical device. One of the physical disk drives is identified for which every logical device mapped to that physical disk drive has been assigned the same power profile. The identified physical disk drive is operated in the mode of operation associated with said same power profile. The mapping of the logical devices to the physical disk drives may be changed in order to group logical devices assigned the same power profile onto the same physical disk drive.

System and method of managing and optimizing power consumption in a storage system

Restricting access to a device includes obtaining a passphrase for a user, obtaining access credentials for the user, obtaining a PIN value for the user, one-way encrypting at least the access credentials and the PIN using a secret key provided with the device. The user is allowed access to the device if the result of one-way encrypting equals the passphrase. The user is denied access to the device if the result of one-way encrypting does not equal the passphrase. The device may be a storage device. The access credentials may include a username that identifies the user and may include a role for the user. The access credentials may include a time period for which access is allowed. Access may be subsequently revoked in response to the current date and time being greater than the time period for which access is allowed.

Accessing data storage devices

Described is a process and device for accessing data stored in multiple logical volumes. The data are replicated on first and second storage elements, such as the redundant hard disk drives of a disk mirror. The multiple logical volumes are divisible into a first logical volume and a second logical volume. All read requests targeting the first logical volume are directed to one of the first and second storage elements. Read requests targeting the second logical volume are asymmetrically interleaved between the first and second storage elements. An asymmetric interleave ratio is determined and implemented that substantially balances the read requests to the multiple logical volumes between the first and second storage elements.

Asymmetrically interleaving access to redundant storage devices

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Brookline, MA, United States of America

Ron Arnan