Emc IP Holding Company LLC

Dell Products Inc.

Benjamin Randolph

Owen Martin

Malak Alshawabkeh

Michael Ferrari

Jaeyoo Jung

Arieh Don

Michael J Scharland

Rong Yu

Deepak Vokaliga

Michael E Specht

Krishna Deepak Nuthakki

Narasimha R Challa

Ian Adams

Abhilash Sanap

Sichao Zhu

Scott J Romano

Hani Zaitoun

A method for use in a storage system, comprising: detecting a first event, the first event being a cyber threat (CT) event, the first event being generated, at least in part, by using security software that is executed in the storage system; in response to the CT event, identifying a secure snapshot of the storage system and performing a first modification of a retention period of the secure snapshot in response to the first event; detecting a second event, the second event being detected after the first modification is performed; and performing a second modification of the retention period in response to the second event.

Automatic retention lock extensions

One or more aspects of the present disclosure relate to autonomous write fencing for backup power. In embodiments, an input/output (IO) workload is received at a storage array. Additionally, one or more IO write requests in the IO workload are cached based on a charge level of a backup power supply of the storage array. Further, the cached IO write requests are processed.

Dataset integrity protection

A profile of characteristics of a normal snapshot of a storage object is computed from previous snapshots of the storage object. Characteristics of a new snapshot are compared with the characteristics in the profile to identify an anomaly indicative of a malicious attack. The characteristics include percent data changed between consecutive snapshots, write LBA dispersion, write data reducibility, number and size of writes, write workload profile, and write content profile. The new snapshot is only vaulted or air-gapped if all its characteristics are within ranges defined by the profile.

Using snapshots for anomaly detection

Techniques for predicting states may include: receiving data sets of counter values, wherein each counter values denotes a number of times a particular code flow point associated with the counter value is executed at runtime during a specified time period; receiving images generated from the data sets; labeling each of the images with state information, wherein first state information associated with a first image indicates that the first image is associated with a first error state of a system or an application; training a neural network using the images to recognize the first state; receiving a next image generated from another data set; and predicting, by the neural network and in accordance with the next image, whether the system or the application is expected to transition into the first state. In at least one embodiment, the foregoing processing may optionally use matrices generated from the data sets rather than images.

Predicting failures with code flow tensors

Techniques for determining service levels may include receiving an I/O operation at a target port of a data storage system, where the I/O operation is sent from an initiator port of a host and directed to a storage object in a first storage group. The initiator port may be included in a first initiator group and the target port may be included in a first port group. The I/O operation may be assigned a service level using a table of a service levels specified for different triples. Each triple may identify an initiator groups, a storage group and a port group. Assigning the service level may include determining a first of the triples that matches the first storage group, the first initiator group and the first port group, wherein the first triple is associated with the service level.

Multi-dimensional I/O service levels

Embodiments of the present disclosure relate to load balancing application processing between storage platforms. Input/output (I/O) workloads can be anticipated during one or more time-windows. Each I/O workload can comprise one or more I/O operations corresponding to one or more applications. Processing I/O operations of each application can be dynamically migrated to one or more storage platforms of a plurality of storage platforms based on the anticipated workload.

Dynamic application migration across storage platforms

A primary storage system appends a red-hot data indicator to each track of data transmitted on a remote data facility during an initial synchronization state. The red-hot data indicator indicates, on a track-by-track basis, whether the data associated with that track should be stored as compressed or uncompressed data by the backup storage system. The red-hot data indicator may be obtained from the primary storage system's extent-based red-hot data map. If the red-hot data indicator indicates that the track should remain uncompressed, or if the track is locally identified as red-hot data, the backup storage system stores the track as uncompressed data. If the red-hot data indicator indicates that the track should be compressed, the backup storage system compresses the track and stores the track as compressed data. After the initial synchronization process has completed, red-hot data indicators are no longer appended to tracks by the primary storage system.

Method and apparatus for selective compression of data during initial synchronization of mirrored storage resources

A method is used in managing remote replication in storage systems. The method monitors network traffic characteristics of a network. The network enables communication between a first storage system and a second storage system. The method predicts a change in at least one of an application demand of an application of a set of applications executing on the first storage server and a network state of the network, where the set of applications have been identified for performing a replication to the second storage system. Based on the prediction, the method dynamically manages replication of the set of applications in accordance with a performance target associated with each application.

Managing remote replication in storage systems

Techniques for processing data may include: determining a first amount denoting an amount of write pending data stored in cache to be redirected through storage class memory (SCM) when destaging cached write pending data from the cache; performing first processing that destages write pending data from the cache, the first processing including: selecting, in accordance with the first amount, a first portion of write pending data that is destaged from the cache and stored in the SCM and a second portion of write pending data that is destaged directly from the cache and stored on one or more physical storage devices providing back-end non-volatile physical storage; and subsequent to storing the first portion of write pending data to the SCM, transferring the first portion of write pending data from the SCM to the one or more physical storage devices providing back-end non-volatile physical storage.

Increasing performance of write throughput using machine learning

Techniques for processing I/O operations may include: receiving an I/O having an associated expected execution time (EET) and I/O service level; selecting, in accordance with the EET and service level of the I/O, a first I/O queue from multiple pending I/O queues; inserting the I/O into the first I/O queue; and performing I/O shifting. I/O shifting may include shifting I/Os from one pending I/O queue to another that is ranked immediately higher than the one pending I/O queue. The multiple I/O queues may be ranked from a highest priority queue to a lowest priority queue. I/O shifting may shift I/Os from the highest priority queue to execution and shifting I/Os from another queue into the highest priority queue. I/O shifting is subject to remaining credits available of the multiple I/O queues and time distance between source and target queues.

Multi-level time decay storage queue

Described are techniques user to determine expected service levels. A first set of one or more data portions are selected from one or more devices forming a first device set. The first set of data portions are stored on physical storage of a physical device set of one or more physical devices having an unknown service level expectation. I/O operations directed to the first set of data portions are received and serviced for a time period. First information is collected that characterizes performance of the physical device set during the time period. In accordance with the first information and using an adaptive learning technique, a first service level expectation for servicing I/Os directed to the physical device set is determined.

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Uxbridge, MA, United States of America

Benjamin Randolph