Netapp, Inc.

Jagadish Vasudeva

Siddhartha Nandi

Parag Deshmukh

Mrinal K Bhattacharjee

Atul Goel

Dnyaneshwar Nagorao Pawar

Priya Sehgal

Amit Valjibhai Panara

Prateeksha Varshney

Srinivasan Narayanamurthy

Vani Vully

Tomislav Grcanac

Ritika

Deepak Dangi

Raj Kamal

Sateesh Kumar Pola

Bharath Kumar K M

Jaldhi Dave

Gururaj Mj

Techniques are provided for aggregate inline deduplication and volume granularity encryption. For example, data that is exclusive to a volume of a tenant is encrypted using an exclusive encryption key accessible to the tenant. The exclusive encryption key of that tenant is inaccessible to other tenants. Shared data that has been deduplicated and shared between the volume and another volume of a different tenant is encrypted using a shared encryption key of the volume. The shared encryption key is made available to other tenants. In this way, data can be deduplicated across multiple volumes of different tenants of a storage environment, while maintaining security and data privacy at a volume level.

Aggregate inline deduplication with volume granular encryption

Systems and methods for supporting dynamic disk growth within a virtual storage appliance are provided. According to one embodiment, a portion of a logical size of respective hyperscale disks provided by a hyperscaler are provisioned for use by a virtual storage system as backing for respective file system disks. To accommodate growth, block numbers for the file system disks are pre-allocated within a sparse space of a contiguous sequence of block numbers corresponding to a number of blocks represented by the logical size. Metadata is maintained for the file system disks regarding a range of the pre-allocated block numbers that are available for use. Responsive to a triggering condition, the provisioned portion of a hyperscale disk is increased and subsequently, responsive to detecting a change in a size of the hyperscale disk by the virtual storage system, a size of the corresponding file system disk is updated within the metadata.

Increasing provisioned storage capacity by means of growing underlying disks within a virtual storage appliance

Systems and methods for reducing read application in a virtual storage system are provided. According to one embodiment, heuristic data may be tracked and utilized in real-time by a file system of the virtual storage system at the level of granularity of a volume, thereby allowing a fast path flag to be enabled/disabled at a volume level during various phases of operation of a workload. The heuristic data for a given volume may be indicative of a correlation between (i) data blocks stored on the given volume being located within a compressible zone of a zoned checksum scheme and (ii) the respective data blocks containing compressed data and a corresponding checksum. Based on the heuristic data, read requests may be selectively directed to the read path (e.g., a fast path or a slow path) expected to mitigate read amplification when data compression is enabled for a zoned checksum scheme.

Read amplification reduction in a virtual storage system when compression is enabled for a zoned checksum scheme

A method, computing device, and non-transitory machine-readable medium for detecting malware attacks and mitigating data loss. In various embodiments, an agent is implemented in the operating system of a storage node to provide protection at the bottommost level in a data write path. The agent intercepts write requests and observes file events over time to detect anomalous behavior. For example, the agent may monitor incoming write requests and, when an incoming write request is detected, determine whether the file is associated with a malware attack risk based on an analysis of an encryption state of data in the file.

Systems and methods for protecting against malware attacks

Systems and methods for reducing read application in a virtual storage system are provided. According to one embodiment, read amplification is reduced when AZCS compression is being utilized by avoiding restarting of a read process via a slow path via a RAID layer of the virtual storage system when a data block associated with a read request and obtained via a first fast path read has been found not to be compressed. Instead, a second fast path read may be performed to obtain the corresponding checksum. Alternatively, or additionally, heuristics may be used to predict the odds of the data block being compressed. For example, when information encoded within a PVBN of the data block that identifies the PVBN as being within a compressed AZCS zone has shown to be sufficiently/insufficiently predictive of the data block being compressed, then a flag may be set to enable/disable fast path reads.

A method, computing device, and non-transitory machine-readable medium for detecting malware attacks and mitigating data loss. In various embodiments, an agent is implemented in the operating system of a storage node to provide protection at the bottommost level in a data write path. The agent intercepts write requests and observes file events over time to detect anomalous behavior. For example, the agent may monitor incoming write requests and, when an incoming write request is detected, determine whether the file is associated with a malware attack risk based on an analysis of an encryption state of data in the file. If the file is associated with a malware attack risk, an entry for the file is added to a file log. The agent may analyze the chi-square values for data written to the files, the file log, and the file format to determine whether a malware attack is underway.

Presented herein are methods, non-transitory computer readable media, and devices for maximizing parallelization in a parity de-clustered and sliced disk RAID architecture implemented on at least one hard disk drive by creating at least one allocation group, each created allocation group comprising at least one parity group within a sliced disk group, selecting one of said at least one allocation group, and performing at least one of write or read concurrently on all parity groups within the selected allocation group.

Efficient method of combining parity groups for uniform load distribution and maximizing parallelization in parity de-clustered and sliced disk raid architecture

A data access request is received specifying a data block stored in a stripe of a parity group that includes a plurality of data storage devices to store data blocks and a parity storage device to store parity information for the data. The stripe includes a data block from each of the plurality of data storage devices and the stripe includes a parity block from the parity storage device. An error is detected in the data block specified by the data access request. The error is identified as a lost write error for the data block or a lost write error for the parity block. Identifying the error includes comparing a first storage device signature stored in a metadata field associated with the data block to a second storage device signature stored in a label block identifying a data storage device where the data block is stored.

Identifying lost write errors in a raid array

A storage server stores data in a stripe of a parity group that includes a plurality of data storage devices to store data and a parity storage device to store parity information. The stripe includes a data block from each of the data storage devices and a parity block from the parity storage device. The storage server receives a data access request specifying a data block in the stripe, and a lost write detection module detects an error in the data block. The lost write detection module compares a first storage device signature stored in a metadata field associated with the data block to a second storage device signature stored in a global field of the data storage device containing the data block. If the first storage device signature matches the second storage device signature, the lost write detection module compares a consistency point count stored in the metadata field to a reconstructed consistency point count. If the reconstructed consistency point count is greater than the consistency point count stored in the metadata field, the lost write detection module identifies the error as a lost write error for the data block. Accurately detecting and identifying the location of a lost write allows the storage server to correct the error and provide a user with a complete and accurate set of data.

Growing community of inventors

Shimoga, India

Jagadish Vasudeva