Emc IP Holding Company LLC

Dell Products Inc.

Emc Corporation

Owen Martin

Malak Alshawabkeh

Arieh Don

Adnan Sahin

Hui Wang

Sean C Dolan

Xiaomei Liu

Ramesh Doddaiah

Marik Marshak

Alexandr Veprinsky

John Creed

Benjamin Randolph

Michael J Scharland

Amnon Naamad

Alex Veprinsky

Dustin Hunt Zentz

Vladimir Desyatov

Dan Aharoni

Jeffrey Wilson

Jaeyoo Jung

Philip Derbeko

Alesia Tringale

Jeremy O'Hare

Earl Medeiros

Peter Blok

Mark D Moreau

Sanjib Mallick

Parmeshwr Prasad

Stephen Smaldone

Rômulo Teixeira De Abreu Pinho

Sachin More

Jonas Furtado Dias

Michael E Specht

Shaul Dar

Gabriel Benhanokh

Ahmet Kirac

Paras Pandya

Philippe Armangau

Vamsi K Vankamamidi

Vinay G Rao

Mark J Halstead

Rahul Deo Vishwakarma

Benjamin W Yoder

Erik P Smith

Tiago Salviano Calmon

Kuolin Hua

Felix Shvaiger

Vinícius Michel Gottin

Adriana Bechara Prado

Krishna Deepak Nuthakki

Andrew Chanler

Eduardo Vera Sousa

Vasudevan Subramanian

Igor Achkinazi

Peter Beale

Anoop Raghunathan

Robert DeCrescenzo

Lev Knopov

James L Davidson

Diego Salomone Bruno

Kaustubh S Sahasrabudhe

Fatemeh Azmandian

Rasa Raghavan

Abhilash Sanap

Benjamin A F Randolph

Ken Dorman

Sichao Zhu

Thomas F O'Neill

Sean B Reilly

Mario Würzl

Benjamin Allen Fitz Randolph

Christopher G LeClair

Hani Zaitoun

Scott J Romano

William R Smith-Vaniz

Peter Linden

A method, comprising: obtaining a parsed trace log that is generated by parsing a raw trace log of a storage system, the parsed trace log identifying a first sequence of tasks that are executed as part of a same input-output (I/O) operation; classifying the first sequence of tasks by using a machine learning model, the first sequence of tasks being classified into one of a first category and a second category, the first category corresponding to anomalous events, and the second category corresponding to non-anomalous events; when the first sequence of tasks is classified in the first category: identifying an anomaly code that corresponds to the first sequence of tasks and generating an alert that is associated with the anomaly code, the anomaly code being identified based on a distance between the first sequence of tasks and a second sequence of tasks that is mapped to the anomaly code.

I/O path anomaly detection

One or more aspects of the present disclosure relate to read-skew-based data theft mitigation. In embodiments, an input/output (IO) workload is received at a storage array. Further, access to a logical device can be controlled based on at least one read capacity skew forecast corresponding to the logical device.

Read skew based data theft mitigation

Autonomous power control is provided on a storage system to enable service level maximum response time controls to be achieved, while also minimizing power consumption, by enabling service level minimum response times to be specified and enforced. A workload/CPU clock speed model is created for the storage system correlating a maximum number of IOPS that the storage system can process for different CPU clock speeds for each workload type. A service level agreement specifies a maximum storage system response time. A storage system minimum response time is also specified, that is used to identify a target CPU clock speed for the workload type being provided by a host. The CPU clock speed is lowered to reduce energy consumption by the storage system toward the target CPU clock speed, and storage system performance is monitored to ensure that the storage system complies with the storage system maximum response time.

Autonomous power control based on service level compliance

Streaming machine telemetry (SMT) event counters are placed in critical code paths of software executing on a storage system. Each monitoring interval the values of the SMT counters are reported. When a critical error occurs on the storage system, a time series set of SMT counters from a set of previous monitoring intervals is labeled with the error type and used as a training example for a learning process. The learning process is trained to learn to learn recursions between time series sets of SMT counter values and labeled error types. Once trained, a checkpoint of the learning model is deployed as an inference model and used to predict the likely occurrence of errors before the errors occur. Predicted errors are logged into a proactive service request queue, and feedback related to the predicted errors are used as feedback to continue training the learning process.

Proactive service requests for forecast storage system failures

In a storage system in which processor cores are exclusively allocated to run process threads of individual emulations, the allocations of cores to emulations are dynamically reconfigured based on forecasted workload. A workload configuration model is created by testing different core allocation permutations with different workloads. The best performing permutations are stored in the model as workload configurations. The workload configurations are characterized by counts of tasks required to service the workloads. Actual task counts are monitored during normal operation and used to forecast changes in actual task counts. The forecasted task counts are compared with the task counts of the workload configurations of the model to select the best match. Allocation of cores is reconfigured to the best match workload configuration.

Autonomous core affinity

A method and apparatus for enabling dynamic selection between compression algorithms to achieve a target data reduction ratio is provided to predict, on a per-extent basis, whether a write operation targeting a piece of data on a particular extent would be preferentially compressed using either compression algorithm A or compression algorithm B. By selectively implementing compression of particular write operations using compression algorithm B, when it is predicted to achieve a greater compression benefits, it is possible to reduce the overall data reduction ratio achieved by the compression engine while economizing the amount of resources expended by the compression engine. The dynamic selection process determines a per-extent compressibility ratio, which is used in connection with a first threshold to evaluate write operations on a per-extent basis. The dynamic selection processes also determines a per-extent size threshold to select between compression algorithms for particular write operations.

Dynamic selection between compression algorithms to achieve a target data reduction ratio

Feature usage profiles of shared global memory are monitored and the profiles are used to allocate computational resources (CPU cores) between thread groups. A global memory monitor is used to monitor global memory metadata to determine the amount of space currently being consumed by a particular feature or service, and dynamically adjusts the amount of computational resources allocated to the thread group responsible for processing IO operations related to the feature or service. By exploiting an inversely proportional relationship between allocated computational resources and usage of shared global memory, it is possible to monitor feature usage profiles of shared global memory to dynamically allocate computational resources to thread groups. By looking at anomalies in usage of slots of shared global memory, and in a targeted way speeding up the processes working on such slots, it is possible to improve overall utilization of shared global memory.

Method and apparatus for dynamic CPU allocation based on feature usage profiles of shared global memory

A power controller for a data storage system automatically selects different power modes based on changing forecasts of availability of renewable energy. The power modes indicate settings for each of a plurality of hardware resources, such as processors, fans, memory, and drives. At times when the storage system can operate at less than the maximum level of performance in terms of IO latency or IOPS, power consumption of one or more of the hardware resources is reduced to a degree corresponding to availability of renewable energy. For example, the hardware resources may be operated in a relatively higher power mode when renewable energy is available than when renewable energy is unavailable. The storage system may be configured to automatically implement a low power mode when power from all sources is in limited supply, e.g., during a brownout.

Autonomous power management for sustainable storage

A storage system configuration alternation system implements storage system reconfiguration alternation by determining a first set of storage system optimization policies to be applied during periods where the workload on the storage system is primarily transactional, and determining a second set of storage system optimization policies to be applied during periods where the workload on the storage system is primarily bandwidth intensive. The storage system configuration alternation system monitors the storage system workload to identify whether the storage system workload is primarily transactional or primarily bandwidth intensive. Based on the current monitored determination, the configuration alternation system selectively applies either the first or second set of storage system policies. A primarily bandwidth intensive workload may be determined based on a combination of a high percentage read IO operations of maximum read size, low CPU utilization, and high back-end bandwidth utilization. Policies may include CPU bias, memory segmentation, and LRU bias.

Storage system reconfiguration alternation for processing intensive workloads and bandwidth intensive workloads

One or more aspects of the present disclosure relate to isolating faults corresponding to a thread or thread group to avoid dropping a director processor slice or board. In embodiments, an execution flow corresponding to a slice on a director board of a storage array is monitored. For example, the execution flow includes a thread or a thread group. Additionally, an exception event corresponding to the execution flow is identified. Further, the execution flow is isolated based on an analysis of the exception event.

Fault isolation in a director processor slice or board

One or more aspects of the present disclosure relate to increasing the performance of a storage array using activity and compressibility-based data compression. In embodiments, an input/output (IO) workload is received at a storage array. Additionally, at least one address space of at least one logical storage device with an IO activity corresponding to an activity characteristic and compressibility corresponding to a compressibility characteristic is identified. Further, if a data reduction requirement (DRR) of the storage array is unsatisfied, IO write requests of the IO workload targeting the identified address spaces of each logical device are compressed.

Activity and compressibility based data compression

Application discovery from access patterns is disclosed. Access histories from multiple servers are collected and stored at a warehouse, which may be part of a data protection system. A time series analysis is performed on the access history to identify consistency groups and applications from the perspective of devices and storage arrays. Data protection operations such as backup operations can then be performed on the basis of devices or storage in storage arrays or other arrangements that pertain to specific consistency groups or to specific applications.

Application discovery using access pattern history

One or more aspects of the present disclosure relate to extending the endurance of flash media. In embodiments, an input/output (IO) workload can be received at a storage array. In addition, one or more characteristics of each write-IO in the IO workload can be determined. Further, for each write-IO, a portion of a storage device of a plurality of storage devices can be dynamically allocated based on the one or more characteristics and an endurance level of each of the plurality of storage devices.

Extending flash media endurance

A data storage system includes a tuner that obtains data samples for data storage operations of workloads and calculates feature measures for a set of features of the data storage operations over aggregation intervals of an operating period. It further (1) applies a cluster analysis to the feature measures to define a set of clusters, and assigns the feature measures to the clusters, and (2) applies a classification analysis to the feature measures labelled by their clusters to identify dominating features of each cluster, and generates workload profiles for the clusters based on the dominating features, and then automatically adjusts configurable processing mechanisms (e.g., caching or tiering) based on the workload profiles and performance or efficiency goals.

Data storage system with self tuning based on cluster analysis of workload features

Embodiments of the present disclosure relate to active data placement. An input/output (I/O stream of data operations is monitored. One or more data units corresponding to each data operation is identified. Based on the identified one or more data units a data structure comprising a list of data unit records is updated, a determination is made whether to transfer one or more data units in the list of data unit records from a slow media class storage to a fast media class storage, a determination is made whether to transfer one or more data units in the fast media class storage to the slow media class storage, and the one or more data units in the list of data unit records is transferred between the fast media class storage and the slow media class storage based on the determinations.

Instance based active data storage management

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Hopedale, MA, United States of America

Owen Martin