Netapp, Inc.

Daniel David McCarthy

Christopher Cason

Ananthan Subramanian

Austino Nicholas Longo

Christopher Clark Corey

Arindam Banerjee

Sneheet Kumar Mishra

Ravikanth Dronamraju

Abdul Basit

Jian Hu

Kevin Daniel Varghese

David D Wright

Mark David Olson

James Philip Wittig

Jared Cantwell

Bharadwaj Vellore Ramesh

Yash Hetal Trivedi

Forrest Trimbell

William Arthur Gutknecht

Swaroop Vikram Choudhari

Ankit Kumar

Chad Edmund Schneider

Lisa Week

In various examples, data storage is managed using a distributed storage management system that is resilient. Data blocks of a logical block device may be distributed across multiple nodes in a cluster. The logical block device may correspond to a file system volume associated with a file system instance deployed on a selected node within a distributed block layer of a distributed file system. Each data block may have a location in the cluster identified by a block identifier associated with each data block. Each data block may be replicated on at least one other node in the cluster. A metadata object corresponding to a logical block device that maps to the file system volume may be replicated on at least another node in the cluster. Each data block and the metadata object may be hosted on virtualized storage that is protected using redundant array independent disks (RAID).

Distributed file system that provides scalability and resiliency

Techniques are provided for failing over an aggregate from one file system instance to a different file system instance of a distributed scale-out storage system. The aggregate may be stored within distributed storage that is accessible to a plurality of file system instances of the distributed scale-out storage system. When the aggregate is failed over from a first file system instance to a second file system instance, the first file system instance may still have a valid read lease that allows the first file system instance to serve client I/O, directed to the aggregate, using a cache. In order to prevent the first file system instance from serving stale data from the cache before the read lease expires, state machines and a set of control data are used to ensure that the second file system instance attaches to the aggregate only after the read lease has expired.

Aggregate failover for a distributed scale-out storage system

A method for reducing write latency in a distributed file system. A write request that includes a volume identifier is received at a data management subsystem deployed on a node within a distributed storage system. The data management subsystem maps the volume identifier to a file system volume and maps the file system volume to a set of logical block addresses in a logical block device in a storage management subsystem deployed on the node. The storage management subsystem maps the logical block device to a metadata object for the logical block device on the node that is used to process the write request. The mapping of the file system volume to the set of logical block addresses in the logical block device enables co-locating the metadata object with the file system volume on the node, which reduces the write latency associated with processing the write request.

Distributed file system with reduced write and read latencies

Techniques are provided for dynamically implementing quality of service policies for a distributed storage system based upon resources saturation. A quality of service policy is defined for throttling I/O operations received by a node of the distributed storage system based upon whether resources of the node have become saturated. The quality of service policy is dynamically implemented based upon ever changing resource utilization and saturation. Dynamically implementing the quality of service policy improves the ability to efficiently utilize resources of the node compared to conventional static polices that cannot adequately react to such changing considerations and resource utilization/saturation. With conventional static policies, an administrator manually defines a minimum amount of guaranteed resources and/or a maximum resource usage cap that could be set to values that result in inefficient operation and resource starvation. Dynamically implementing the quality of service policy results in more efficient operation and mitigates resource starvation.

Dynamic quality of service implementation based upon resource saturation

Techniques are provided for dynamically implementing quality of service policies using a configurable quality of service provider pipeline. A quality of service policy is defined for throttling I/O operations received by a node based upon whether resources of the node have become over utilized. The quality of service policy is used to dynamically construct a quality of service provider pipeline with select quality of service providers that improve the ability to efficiently utilize resources compared to conventional static polices that cannot adequately react to changing considerations and resource utilization/saturation. With conventional static policies, an administrator manually defines a minimum amount of guaranteed resources and/or a maximum resource usage cap that could be set to values that result in inefficient operation and resource starvation. Dynamically constructing and utilizing the quality of service provider pipeline results in more efficient operation and mitigates resource starvation.

Configurable quality of service provider pipeline

Systems and methods for scaling application and/or storage system functions of a distributed storage system based on a heterogeneous resource pool are provided. According to one embodiment, the distributed storage system has a composable, service-based architecture that provides scalability, resiliency, and load balancing. The distributed storage system includes a cluster of nodes each potentially having differing capabilities in terms of processing, memory, and/or storage. The distributed storage system takes advantage of different types of nodes by selectively instating appropriate services (e.g., file and volume services and/or block and storage management services) on the nodes based on their respective capabilities. Furthermore, disaggregation of these services, facilitated by interposing a frictionless layer (e.g., in the form of one or more globally accessible logical disks) therebetween, enables independent and on-demand scaling of either or both of application and storage system functions within the cluster while making use of the heterogeneous resource pool.

Dynamically scaling application and storage system functions based on a heterogeneous resource pool available for use by a distributed storage management system

The disclosed technology enables quicker initialization of a new master node for a cluster when a previous master node fails by tracking node state in the cluster prior to being designated the new master node. In a particular example, a method includes, in a first node, designated as a current master node for the cluster, managing the cluster based on states of the nodes determined by the first node. While the first node is designated the master node, the method includes each of the nodes collecting, and storing locally, the states of the nodes. In response to a failure of the first node, the method includes selecting a second node of the nodes a new master node. Upon being designated the new master node, the method includes the second node managing the cluster of nodes based on the states of the nodes that the second node collected and stored locally.

Collection of state information by nodes in a cluster to handle cluster management after master-node failover

A technique is configured to provide various data protection schemes, such as replication and erasure coding, for data blocks of volumes served by storage nodes of a cluster configured to perform deduplication of the data blocks. Additionally, the technique is configured to ensure that each deduplicated data block complies with data redundancy guarantees of the data protection schemes, while improving storage space of the storage nodes. In order to satisfy the data integrity guarantees while improving available storage space, the storage nodes perform periodic garbage collection for data blocks to optimize storage in accordance with currently applicable data protection schemes.

Storage space optimization in a system with varying data redundancy schemes

A distributed storage management system comprising nodes that form a cluster, a distributed block layer that spans the nodes in the cluster, and file system instances deployed on the nodes. Each file system instance comprises a data management subsystem and a storage management subsystem disaggregated from the data management subsystem. The storage management subsystem comprises a node block store that forms a portion of the distributed block layer and a storage manager that manages a key-value store and virtualized storage supporting the node block store. A file system volume hosted by the data management subsystem maps to a logical block device hosted by the virtualized storage in the storage management subsystem. The key-value store includes, for a data block of the logical block device, a key that comprises a block identifier for the logical block device and a value that comprises the data block.

Techniques are provided for repairing a primary slice file, affected by a storage device error, by using one or more dead replica slice files. The primary slice file is used by a node of a distributed storage architecture as an indirection layer between storage containers (e.g., a volume or LUN) and physical storage where data is physically stored. To improve resiliency of the distributed storage architecture, changes to the primary slice file are replicated to replica slice files hosted by other nodes. If a replica slice file falls out of sync with the primary slice file, then the replica slice file is considered dead (out of sync) and could potentially comprise stale data. If a storage device error affects blocks storing data of the primary slice file, then the techniques provided herein can repair the primary slice file using non-stale data from one or more dead replica slice files.

Slice file recovery using dead replica slice files

A technique provides efficient data protection, such as erasure coding, for data blocks of volumes served by storage nodes of a cluster. Data blocks associated with write requests of unpredictable client workload patterns may be compressed. A set of the compressed data blocks may be selected to form a write group and an erasure code may be applied to the group to algorithmically generate one or more encoded blocks in addition to the data blocks. Due to the unpredictability of the data workload patterns, the compressed data blocks may have varying sizes. A pool of the various-sized compressed data blocks may be established and maintained from which the data blocks of the write group are selected. Establishment and maintenance of the pool enables selection of compressed data blocks that are substantially close to the same size and, thus, that require minimal padding.

Pooling blocks for erasure coding write groups

Systems and methods for quality of service management are provided. According to one embodiment, a non-transitory computer-readable medium comprises instructions that when executed by the processing resource cause the processing resource to receive, in a normalizing agent, one or more compute load parameters from one or more background compute processes executing on the one or more computer systems and one or more Quality of Service (QoS) parameters for one or more client compute processes executing on the one or more computer systems, convert the one or more compute load parameters to one or more normalized utilization metrics, and execute instructions that cause a processor to adjust a compute resource allocation dedicated to the one or more background compute processes based at least in part on the one or more normalized utilization metrics and the one or more QoS parameters.

Centralized quality of service management

A bin syncing technique ensures continuous data protection, such as replication and erasure coding, for content driven distribution of data served by storage nodes of a cluster in the event of failure to one or more block services configured to process the data. The cluster maintains information about the block services assigned to host a bin with a copy of the data in a bin assignment table associated with a state. The copies of the data are named, e.g., replica 0 (R0), replica 1 (R1) or replica 2 (R2). In response to failure of one or more block services assigned to host a bin with a replica of the data, an alternate or replacement block service may access the assignments maintained in the bin assignment table, which specify names of the replicas associated with the state.

Growing community of inventors

Erie, CO, United States of America

Daniel David McCarthy