Author: SNIAOnStorage

Recently, a question came up in the SNIA Computational Storage Special Interest Group on new developments in a technology called eBPF and how they might relate to computational storage. To learn more, SNIA on Storage sat down with Eli Tiomkin, SNIA CS SIG Chair with NGD Systems; Matias Bjørling of Western Digital; Jim Harris of Intel; Dave Landsman of Western Digital; and Oscar Pinto of Samsung.

SNIA On Storage (SOS):  The eBPF.io website defines eBPF, extended Berkeley Packet Filter, as a revolutionary technology that can run sandboxed programs in the Linux kernel without changing kernel source code or loading kernel modules.  Why is it important?

Dave Landsman (DL): eBPF emerged in Linux as a way to do network filtering, and enables the Linux kernel to be programmed.  Intelligence and features can be added to existing layers, and there is no need to add additional layers of complexity.

SNIA On Storage (SOS):  What are the elements of eBPF that would be key to computational storage? 

Jim Harris (JH):  The key to eBPF is that it is architecturally agnostic; that is, applications can download programs into a kernel without having to modify the kernel.  Computational storage allows a user to do the same types of things – develop programs on a host and have the controller execute them without having to change the firmware on the controller.

Using a hardware agnostic instruction set is preferred to having an application need to download x86 or ARM code based on what architecture is running.

DL:  It is much easier to establish a standard ecosystem with architecture independence.  Instead of an application needing to download x86 or ARM code based on the architecture, you can use a hardware agnostic instruction set where the kernel can interpret and then translate the instructions based on the processor. Computational storage would not need to know the processor running on an NVMe device with this “agnostic code”.

SOS: How has the use of eBPF evolved?

JH:  It is more efficient to run programs directly in the kernel I/O stack rather than have to return packet data to the user, operate on it there, and then send the data back to the kernel. In the Linux kernel, eBPF began as a way to capture and filter network packets.  Over time, eBPF use has evolved to additional use cases.

SOS:  What are some use case examples?

DL: One of the use cases is performance analysis. For example, eBPF can be used to measure things such as latency distributions for file system I/O, details of storage device I/O and TCP retransmits, and blocked stack traces and memory.

Matias Bjørling (MB): Other examples in the Linux kernel include tracing and gathering statistics.  However, while the eBPF programs in the kernel are fairly simple, and can be verified by the Linux kernel VM, computational programs are more complex, and longer running. Thus, there is a lot of work ongoing to explore how to efficiently apply eBPF to computational programs.

For example, what is the right set of run-time restrictions to be defined by the eBPF VM, any new instructions to be defined, how to make the program run as close to the instruction set of the target hardware.

JH: One of the big use cases involves data analytics and filtering. A common data flow for data analytics are large database table files that are often compressed and encrypted.  Without computational storage, you read the compressed and encrypted data blocks to the host, decompress and decrypt the blocks, and maybe do some filtering operations like a SQL query.  All this, however, consumes a lot of extra host PCIe, host memory, and cache bandwidth because you are reading the data blocks and doing all these operations on the host.  With computational storage, inside the device you can tell the SSD to read data and transfer it not to the host but to some memory buffers within the SSD.  The host can then tell the controller to do a fixed function program like decrypt the data and put in another local location on the SSD, and then do a user supplied program like eBPF to do some filtering operations on that local decrypted data.  In the end you would transfer the filtered data to the host.  You are doing the compute closer to the storage, saving memory and bandwidth.

SOS:  How does using eBPF for computational storage look the same?  How does it look different?

Jim – There are two parts to this answer.  Part 1 is the eBPF instruction set with registers and how eBPF programs are assembled.  Where we are excited about computational storage and eBPF is that the instruction set is common. There are already existing tool chains that support eBPF.   You can take a C program and compile it into an eBPF object file, which is huge.  If you add computational storage aspects to standards like NVMe, where developing a unique tool chain support can take a lot of work, you can now leverage what is already there for the eBPF ecosystem. 

Part 2 of the answer centers around the Linux kernel’s restrictions on what an eBPF program is allowed to do when downloaded. For example, the eBPF instruction set allows for unbounded loops, and toolchains such as gcc will generate eBPF object code with unbounded loops, but the Linux kernel will not permit those to execute – and rejects the program. These restrictions are manageable when doing packet processing in the kernel.  The kernel knows a packet’s specific data structure and can verify that data is not being accessed outside the packet.  With computational storage, you may want to run an eBPF program that operates on a set of data that has a very complex data structure – perhaps arrays not bounded or multiple levels of indirection.  Applying Linux kernel verification rules to computational storage would limit or even prevent processing this type of data.

SOS: What are some of the other challenges you are working through with using eBPF for computational storage?

MB:  We know that x86 works fast with high memory bandwidth, while other cores are slower.  We have some general compute challenges in that eBPF needs to be able to hook into today’s hardware like we do for SSDs.  What kind of operations make sense to offload for these workloads?  How do we define a common implementation API for all of them and build an ecosystem on top of it?  Do we need an instruction-based compiler, or a library to compile up to – and if you have it on the NVMe drive side, could you use it?  eBPF in itself is great- but getting a whole ecosystem and getting all of us to agree on what makes value will be the challenge in the long term.

Oscar Pinto (OP): The Linux kernel for eBPF today is more geared towards networking in its functionality but light on storage. That may be a challenge in building a computational storage framework. We need to think through how to enhance this given that we download and execute eBPF programs in the device. As Matias indicated, x86 is great at what it does in the host today. But if we have to work with smaller CPUs in the device, they may need help with say dedicated hardware or similar implemented using additional logic to aid the eBPF programs One question is how would these programs talk to them?  We don’t have a setup for storage like this today, and there are a variety of storage services that can benefit from eBPF.

SOS: Is SNIA addressing this challenge?

OP: On the SNIA side we are building on program functions that are downloaded to computational storage engines.  These functions run on the engines which are CPUs or some other form of compute that are tied to a FPGA, DPU, or dedicated hardware. We are defining these abstracted functionalities in SNIA today, and the SNIA Computational Storage Technical Work Group is developing a Computational Storage Architecture and Programming Model and Computational Storage APIs  to address it..  The latest versions, v0.8 and v0.5, has been approved by the SNIA Technical Council, and is now available for public review and comment at SNIA Feedback Portal.

SOS: Is there an eBPF standard? Is it aligned with storage?

JH:  We have a challenge around what an eBPF standard should look like.  Today it is defined in the Linux kernel.  But if you want to incorporate eBPF in a storage standard you need to have something specified for that storage standard.  We know the Linux kernel will continue to evolve adding and modifying instructions. But if you have a NVMe SSD or other storage device you have to have something set in stone –the version of eBPF that the standard supports.  We need to know what the eBPF standard will look like and where will it live.  Will standards organizations need to define something separately?

SOS:  What would you like an eBPF standard to look like from a storage perspective?

JH – We’d like an eBPF standard that can be used by everyone.  We are looking at how computational storage can be implemented in a way that is safe and secure but also be able to solve use cases that are different.

MB:  Security will be a key part of an eBPF standard.  Programs should not access data they should not have access to.  This will need to be solved within a storage device. There are some synergies with external key management. 

DL: The storage community has to figure out how to work with eBPF and make this standard something that a storage environment can take advantage of and rely on.

SOS: Where do you see the future of eBPF?

MB:  The vision is that you can build eBPFs and it works everywhere.  When we build new database systems and integrate eBPFs into them, we then have embedded kernels that can be sent to any NVMe device over the wire and be executed.  The cool part is that it can be anywhere on the path, so there becomes a lot of interesting ways to build new architectures on top of this. And together with the open system ecosystem we can create a body of accelerators in which we can then fast track the build of these ecosystems.  eBPF can put this into overdrive with use cases outside the kernel.

DL:  There may be some other environments where computational storage is being evaluated, such as web assembly.

JH: An eBPF run time is much easier to put into an SSD than a web assembly run time.

MB: eBPF makes more sense – it is simpler to start and build upon as it is not set in stone for one particular use case.

Eli Tiomkin (ET):  Different SSDs have different levels of constraints.  Every computational storage SSDs in production and even those in development have very unique capabilities that are dependent on the workload and application.

SOS:  Any final thoughts?

MB: At this point, technologies are coming together which are going to change the industry in a way that we can redesign the storage systems both with computational storage and how we manage security in NVMe devices for these programs.  We have the perfect storm pulling things together. Exciting platforms can be built using open standards specifications not previously available.

SOS:  Looking forward to this exciting future. Thanks to you all.