DataSphere proactively prevents failure and information loss, and provides the ability to recover when faults do occur. These capabilities provide enterprise reliability, accessibility and serviceability without impact to ongoing I/O.
DataSphere is architected to provide fail overs and backups of its metadata. The metadata is synchronously copied between DataSphere pairs to protect against component failures. In the event that a DataSphere node fails, operation automatically failsover within seconds to a paired DataSphere to ensure there is no disruption to applications I/O. It is important to note that with DataSphere out of the data path, no in-flight data requests are impacted when a failover event occurs. Resiliency is built into the communication protocols between DataSphere and the clients to ensure no metadata requests are lost during the fail over process.
This ability allows non-disruptive software updates as each DataSphere node is updated in an orchestrated (rolling cluster update) sequence that is transparent to clients. Further, the metadata is backed up and can be restored to protect against the most extreme failure events.
The overall DataSphere architecture ensures a higher level of operational performance by eliminating all single points of failure through redundancy, and non-disruptive maintenance during normal operations.
Give Your Applications Intelligent Storage Awareness
Applications and storage have long been blind to each other’s capabilities and needs. The majority (if not nearly all) of today’s enterprise applications do not know the attributes of the storage where its data resides. Applications cannot tell if the storage is fast or slow, premium or low cost.
Conversely, storage does not know what data is the most important to an application. It only knows what was recently accessed, and uses that information to place data in caching tiers, which will increase performance if that same data happens to be accessed again. However, caching tiers do not have the intelligence needed to protect capacity for mission-critical applications, which can cause serious performance inconsistency or require more cache.
DataSphere gathers metadata intelligence in real-time to understand how applications experience storage (for example latency, IOPS and bandwidth). It also collects telemetry on the data that applications access, such as which files are open, closed, with modified dates and times, as well as any other metadata.
Open standard data and I/O access-based protocol stacks in the client make this possible. The recent release of NFS 4.2 includes enhancements to the Parallel Network File System (pNFS) Flex File layout that allow clients to provide statistics on how data is being used, and the performance provided by the storage resources serving the data. These advanced features are already being rapidly adopted, as the most recent release of Red Hat Enterprise Linux 7.3 features Flex Files support to simplify management of Parallel NFS (pNFS) clusters. DataSphere DSX enables the same capabilities with legacy protocols such as NFS v3, SMB 2.1 and SMB 3.x.
With the ability to collect analytics from individual clients on single data objects, DataSphere can analyze application workloads, priority, historical trending, and available storage resources, comparing real-time activity against business objectives defined by IT and application administrators. DataSphere then automates data management for NFS, SMB, VMware ESXi clients across datacenter and cloud based resources, moving data to the most appropriate resource without application interruption.