Symantec Corporation

Veritas Technologies LLC

Symantec Operating Corporation

Nutanix, Inc.

Veritas Operating Corporation

Symantec Operation Corporation

Symantec Operating Corporating

Niranjan Sanjiv Pendharkar

Shailesh Marathe

Anand A Kekre

Angshuman Bezbaruah

Subhojit Roy

Anindya Banerjee

Prasanna V Wakhare

Ronald S Karr

John Colgrove

Oleg Kiselev

Samir Desai

Suhas Ashok Dantkale

Kedar Shrikrishna Patwardhan

Dilip Madhusudan Ranade

Mukund Agrawal

Venkeepuram R Satish

Sumit Kumar

Chaitanya Yalamanchili

Christopher Uhler

Chirag Deepak Dalal

Rahul M Fiske

Shirish Vijayvargiya

Pradip Kulkarni

Sanjay Kumar

Alok Nemchand Kataria

Tabrez Parvez Memon

Satyendra Thakur

Sumit Dighe

Ashish Karnik

Rajesh Chepuri

Michael Wahl

Shubham Shukla

Pete Wyckoff

Jaya Singhvi

Ketan Mahajan

Prem Anand Ramanathan

Shubham Sharma

Naveen Kumar

Feng Wang

Pranab Patnaik

Raghu Krishnamurthy

Sanjay Kumar Jain

Rishi Bhardwaj

Bharat Kumar Beedu

Subash Rajaa

Hari Krishna Vemuri

Michael E Root

Vikas Gupta

Aalop Suresh Shah

Kiran Tatiparthi

Sudhakar Paulzagade

Kirubakaran Kaliannan

Mukul Kumar

Vivek Venkatesan

Ashish Laxmanrao Gawali

Abhishek Narula

David H Lin

Rohit Jain

Anirban Mukherjee

Nirendra Awasthi

Ganesh Varadarajan

Prasad Limaye

Kedar M Karmarkar

Asmita Jagtap

Praveen Kumar Padia

Santosh Kalekar

Venkata Tatavarty

Shweta Goyal

Rakesh Ranjan

Anuj Garg

Subhadeep De

Ashish Agarwal

Arun M Rokade

Shishir S Asgaonkar

Vidyut Kaul

Rishikesh Bhagwandas Jethwani

Phani Karthik Maradani

Naveen Ramachandrappa

Ramya Mula

Sumit Raghunath Dighe

Jiang-Hsing Chu

Dhruv Bhatnagar

Swapnil Ingle

Gaurav Bhandarkar

Amit Jambure

Shilesh Marathe

Mahesh Khatpe

Anandi Deopujari

Ajay Kumar

Nikhil Keshav Sontakke

Ketan Nilangekar

Kati Rader

Puneet Bakshi

Ashish K Saxena

Pooja Sarda

Prasad K Dharap

Anish Jain

Mangesh Panche

Nitin Wankhede

Sumit Sharma

A technique monitors input/output (I/O) and Persistent Reservation (PR) activity patterns to detect degraded performance of a highly available and fault tolerant application executing in a multi-site disaster recovery (DR) environment. Multiple instances of the application execute in different virtual machines (VMs) of a compute layer within a guest clustering configuration that extends across clusters of the sites. A storage layer of the clusters provides shared storage to the multiple VMs across the multiple sites. One of the sites is configured as an active storage site configured to receive and service I/O requests from the compute layer. A single instance of the application is active at a time and configured as a 'compute owner' of the shared storage to issue the I/O requests to the shared storage. The compute owner and active storage site may not be co-located on the same site, leading to excessive I/O and PR activity patterns indicative of degraded performance. Upon detecting such patterns, the technique automatically triggers a storage failover to ensure that compute owner and active storage site are co-located at the same site.

Monitoring input/output and persistent reservation activity patterns to detect degraded performance of a high availability and fault tolerant application

A technique improves implementation of an index for an operations log (oplog) that coalesces random write operations directed to a virtual disk (vdisk) stored on an extent store. The oplog temporarily caches data associated with the random write operations (i.e., write data) as well as metadata describing the write data. The metadata includes descriptors to the write data stored on virtual address regions, i.e., offset ranges, of the vdisk and are used to identify the offset ranges of write data for the vdisk that are cached in the oplog. To facilitate fast lookup operations of the offset ranges when determining whether write data is cached in the oplog, an oplog index provides a state of the latest data for offset ranges of the vdisk. The technique improves implementation of the oplog index by storing the oplog index in storage class memory, such as persistent memory, to obviate failure and subsequent recovery of the oplog index.

Technique for improving operations log indexing

A technique replicates an index of an operations log (oplog) from a primary node to a secondary node of a cluster in the event of failure. The oplog functions as a staging area to coalesce random write operations directed to a virtual disk (vdisk) stored on a backend storage tier. The oplog temporarily caches write data as well as metadata describing the write data. The metadata includes descriptors to the write data corresponding to offset ranges of the vdisk and are used to identify ranges of write data for the vdisk that are cached in the oplog. To facilitate fast lookup operations of whether write data is cached in the oplog, an oplog index provides a state of the latest data for offset ranges of the vdisk that enables fast failover of metadata used to construct the oplog index in memory without downtime or significant metadata replay.

Technique for replicating oplog index among nodes of a cluster

A dynamic feedback technique improves data replication performance by balancing rates of data retrieval and data transmission of a fragmented virtual disk replicated between nodes of clusters on a local site and a remote site of a disaster recovery environment. Each node is embodied as a physical computer with hardware resources, such as processor, memory, network and storage resources, which are virtualized to provide support for one or more user virtual machines executing on the node. The storage resources include storage devices of an extent store, whereas the network includes a wide area network connecting the local and remote sites. The dynamic feedback technique employs a virtual memory buffer configured to balance the data storage retrieval and network transmission rates at a source of replication based on bandwidth demands of the extent store and network throughput as manifested by an available free space (i.e., emptiness) of the virtual buffer.

Dynamic feedback technique for improving disaster recovery replication performance

Methods, systems, and computer program products for moving a container-based application from a source HCl cluster to a target HCl cluster. An HCl storage cluster data replication mechanism operates to copy data and metadata from a plurality of storage devices that constitute a storage pool of the source HCl cluster. A container-based application replication mechanism operates to copy data and metadata from an executable container that is running on a node of the source HCl cluster. Periodically (1) the state of the container-based application is saved using the container-based application replication mechanism, and (2) the state of the storage cluster data of the HCl storage cluster is saved using the HCl storage cluster data replication mechanism. The saved state of the container-based application including its application data and metadata are combined with the saved state of the storage cluster data to bring-up the container-based application at the target HCl cluster.

Container-based application processing

Storage locations in a first tier of a multi-tier storage system are allocated to a first set of data structures (e.g., inodes) in a first file set. A file that is stored in the first tier is associated with a first data structure of the first set. In response to determining that data in the file should be moved to a second tier of the multi-tier storage system, the file is associated with a second data structure in a second file set. The second data structure is allocated a storage location in the second tier. Consequently, two data structures are associated with the file. The data is copied from the first tier to the storage location in the second tier, and can be subsequently accessed using the second data structure.

Multi-tier storage using multiple file sets

The disclosed computer-implemented method for automatic storage tiering may include (1) receiving characteristics of previous accesses to storage system objects stored in a data storage system including multiple storage tiers, (2) generating, based on the characteristics of previous accesses to the storage system objects, a model that predicts characteristics of future accesses to the storage system objects, (3) selecting, based on the model, a next storage tier of the multiple storage tiers for each of the storage system objects, and (4) relocating at least some of the storage system objects from a current storage tier to the next storage tier selected for each of the at least some of the storage system objects. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for automatic storage tiering

The disclosed computer-implemented method for managing quality of service may include (i) installing, at a network protocol layer, a latency monitoring plug-in that monitors latency at the network protocol layer as distinct from a storage layer beneath the network protocol layer, (ii) providing, by the latency monitoring plug-in, feedback reporting the latency at the network protocol layer to a quality of service engine that monitors characteristics of the storage layer such that the quality of service engine learns about the latency at the network protocol layer, and (iii) adjusting, by the quality of service engine, a calculation of system capacity, based on both the feedback from the latency monitoring plug-in reporting the latency at the network protocol layer and the monitored characteristics of the storage layer to enable an application to adhere to a service level agreement. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for managing quality of service

The disclosed computer-implemented method for managing quality of service may include (i) providing, in a network storage environment, a quality of service engine to regulate network traffic by assigning three separate factors to applications that are consuming a network resource, (ii) adding, to a configuration of the quality of service engine, a fourth priority factor that establishes two tiers of priority among applications that generate traffic that is regulated by the quality of service engine, and (6) regulating, by the quality of service engine based on the fourth priority factor, network traffic in the network storage environment by providing access to the network resource to an application assigned a higher priority over another application assigned a lower priority such that the fourth priority factor enables the quality of service engine to adhere to a service level agreement. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method for distributing cache space may include (i) identifying workloads that make input/output requests to a storage system that comprises a cache that stores a copy of data recently written to the storage system, (ii) calculating a proportion of the cache that is occupied by data written to the cache by a workload, (iii) determining that the proportion of the cache that is occupied by the data written to the cache by the workload is disproportionate, and (iv) limiting the volume of input/output requests from workload that will be accepted by the storage system in response to determining that the proportion of the cache that is occupied by the data written to the cache by the workload is disproportionate. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for distributing cache space

A method for flushing data in a virtual computing environment is provided. The method includes writing application output from an application spanning one or more virtual machines to a cache, wherein each virtual machine is implemented using one or more compute nodes and the cache is implemented in storage associated with the one or more compute nodes. In an effort to flush data associated with application writes more efficiently from the cache to a local backing store or one or more data nodes, the method may include generating a mapping of each of the plurality of application writes. The method may further include sorting sets of one or more virtual disks based upon an offset of each application write as indicated in the mapping. In addition, a storage virtualizer may cancel duplicate application writes, merge multiple contiguous application writes, and merge multiple epochs prior to flushing the data.

Systems and methods for flushing data in a virtual computing environment

A method for implementing continuous data replication in a distributed computer system. The method includes receiving a forwarded data object write I/O from a data server. The method further includes examining attribute information of the data object to determine a consistency group for the data object. A replication policy is examined in view of the consistency group of the data object to determine a destination redundancy server. The data object write I/O is forwarded to the destination redundancy server for storage. An acknowledgment is then sent to the data server upon storage of the data object in the destination redundancy server.

Continuous replication in a distributed computer system environment

A method for two level quality of service scheduling for latency and queue depth control in a storage system is provided. The method includes determining queue depth as input/output operations per unit time that a storage subsystem can sustain, for each of a plurality of queues corresponding to storage subsystems in the storage system and determining system capacity as total input and output operations per unit time that the storage system can sustain, based on the queue depth of each of the plurality of queues. The method includes determining whether to accept or deny an input/output request for placement onto an input/output stack, based on a comparison of credits of the input/output request to the system capacity as determined on an ongoing basis, and placing the input/output request from the input/output stack onto one of the plurality of queues based on whether doing so maintains the queue depth of the one of the plurality of queues.

Two level QoS scheduling for latency and queue depth control

The disclosed computer-implemented method for improving quality of service within hybrid storage systems may include (1) monitoring a performance measurement of a hybrid storage system that includes first and second types of storage devices that handle I/O throughput in connection with an application, (2) determining, based at least in part on the monitored performance measurement, an approximate amount of the I/O throughput handled by the first type of storage device over a period of time, (3) determining, based at least in part on the approximate amount of I/O throughput, a rate at which the application is allowed to deliver subsequent I/O throughput to the hybrid storage system over a subsequent period of time, and then (4) regulating, based at least in part on the rate, the subsequent I/O throughput in connection with the application over the subsequent period of time. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for improving quality of service within hybrid storage systems

A method for creating snapshots and backups in a virtual computing environment is provided. The method includes writing application output of an application spanning one or more virtual machines as an application consistency group to a writeback cache, wherein the one or more virtual machines are implemented using one or more compute nodes and wherein the writeback cache is implemented in direct attached storage in the one or more compute nodes. The method includes pausing I/O (input/output) operations of the application and marking the pausing, in the writeback cache. The method includes resuming the I/O operations of the application, after the marking and dumping data, according to the marking, from the writeback cache to a data node, as a snapshot.

System and method for creating snapshots in openflame environment

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Maharashtra, India

Niranjan Sanjiv Pendharkar