Understanding the I/O Path of a Storage Acceleration Solution

Understanding the I/O Path of a

Storage Acceleration Solution

A technical deep dive into how reads and writes are accelerated in a

decoupled storage architecture

This PernixData whitepaper was compiled using blog posts written by Frank Denneman

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Accelerating Storage Performance

As storage performance continues to plague virtual data centers, storage acceleration has become a hot topic within many IT organizations. For the first time ever, server-side acceleration solutions exist that let companies decouple storage performance from storage capacity, delivering optimal application performance at substantial cost savings.

A decoupled storage acceleration architecture creates unique requirements that differ substantially from traditional storage environments (where data services and storage performance are co-mingled). This paper discusses these architectural differences, highlighting the impact they have on the performance of both read and write intensive virtualized applications.

Acceleration layer architecture and I/O requests

In a traditional storage architecture, the I/O path of data to and from a storage array is pretty straightforward. For example, when an application in a VM generates a read command (data block A), the command is issued to the storage array, which then sends the data inside block A to the ESXi host, who then sends it to the virtual machine.

ESXi Host VM Storage Array Read “A” Read “A” A A A

Figure 1 I/O path in traditional shared storage architecture.

As the data exists only in one place, the I/O command follows a predictable path through the architecture. Performance is easy to determine, as the predictable path produces repeatable results.

I/O Path with an Acceleration Platform

A server-side acceleration platform, such as PernixData FVP™, creates an additional layer between the virtual machine and the storage array. It keeps copies of recently read or written data in high speed server resources, such as flash or RAM, to accelerate read and write operations.

VM

VM VM VM VM VM VM

FVP ESXi Host

Flash Device/RAM

ESXi Host ESXi Host

Storage Array

VM VM

Flash Device/RAM Flash Device/RAM

Figure 2 Storage acceleration platforms create an additional acceleration layer between VMs and storage to accelerate reads and writes

The PernixData FVP platform virtualizes server flash and RAM across hosts into one seamless acceleration tier, allowing virtual machines to read from and write data to this layer. Depending on the FVP write policy, data will be committed to the storage array directly or destaged later.

The PernixData FVP platform provides two policies for accelerating traffic: Write Through and Write Back, each of which treats write data very differently.

Write Through Policy for Storage Acceleration

VM ESXi host Storage Array Path

Flash Device/

RAM

4 The Write Through policy intercepts write data originating from the application and stores this data on the flash

device or RAM to satisfy future read requests for that data. Simultaneously, the data is sent to the storage system. When the PernixData FVP platform receives the acknowledgment from both locations that the data is stored safely, the ESXi hypervisor will inform the application that the write operation is completed. Typically, the longest time to acknowledgment is from the storage array.

Write Back Policy for Storage Acceleration

Storage System VM ESXi host Storage Array Path

Flash Device/

RAM

Figure 4 In write back mode, writing data to the storage array is a separate process from writing to the flash device or RAM. After receiving acknowledgment from the flash device/RAM, the write operation is complete.

The Write Back policy intercepts write data originating from the application and stores this data on the flash de-vice or RAM. After receiving the acknowledgment from the flash dede-vice/RAM, the ESXi hypervisor will inform the application that the write operation is complete. Independently, FVP software sends the write data to the storage array. Writing the data to the storage array is a separate process from writing to the flash device/RAM, which is completely transparent to the application.

Accelerating Writes

Write Back has the biggest impact on write performance, because data is written to the flash device/RAM on the host without requiring acknowledgments from the storage array. This creates a very short I/O path, resulting in very low latencies (typically microseconds).

Write Through requires writes to cross the storage fabric to get to the storage array (and be acknowledged) before proceeding. This does not provide the same low latency I/O path as Write Back. But, because the storage acceleration platform is offloading a significant number of reads from the storage array, this liberates the array to handle writes. As a result, some write performance gains can be achieved in this mode, too.

Accelerating Reads

Both policies accelerate reads in the same manner. The most important thing to look at is how the requested data is presented on the flash device/RAM, a concept known as “read access”. This can be done in two ways: By storing write data directly on the flash device/RAM, or by retrieving the data from the storage array and simultaneously copying it to the flash device/RAM when presenting the data to the application.

Storage System VM ESXi host Storage Array Path

Flash Device/

RAM

Figure 5 If read data is not already in flash/RAM, it will be retrieved from the storage array and copied to flash/RAM when presented back to the application.

Once the data is stored on the flash device/RAM, subsequent I/O operations requesting this data can be rapidly serviced by the flash device/RAM – a concept known as a “hit”. High hit rates translate into reduced latency, and much better read performance over time.

Conclusion

A server-side acceleration platform, such as PernixData FVP , creates a layer between the virtual machine and the storage array that is used to accelerate both reads and writes to primary storage. This creates a high-performance I/O path compared to traditional storage environments, resulting in substantially faster applications and true scale-out storage performance.