114: GreyBeards talk computational storage with Tong Zhang, Co-Founder & Chief Scientist, ScaleFlux

Seeing as how one topic on last years FMS2020 wrap-up with Jim Handy was the rise of computational storage and it’s been a long time (see GreyBeards talk with Scott Shadley at NGD Systems) since we discussed this, we thought it time to check in on the technology. So we reached out to Dr. Tong Zhang, Chief Scientist and Co-founder, ScaleFlux to see what’s going on. ScaleFlux is seeing rising adoption of their product in hyper-scalers as well as large enterprises. Their computational storage is a programmable FPGA based 4TB and 8TB SSD.

Tong was very knowledgeable on current industry trends (Moore’s law slowing & others) that have created an opening for computational storage and other outboard compute. He also is well versed into how some of the worlds biggest customers are using the technology to work faster and cheaper in their data centers. Listen to the podcast to learn more.

At the start Tong mentioned Alibaba’s use of ScaleFlux’s transparent, line speed, outboard encryption/decryption and compression/decompression. And, depending on the data, they can see compression ratios far exceeding 2:1. As such, customers not only benefit from a cheaper $/GB but can also see better NAND endurance and higher performance.

Hosts can do compression and encryption but doing so takes a lot of CPU cycles. It turns out that compression is more compute intensive than encryption. Tong said that most modern cores can encrypt/decrypt at 1GB/sec but, depending on the compression algorithm, can only compress at 40 to 100MB/sec. But in any case doing so on the host consumes a lot of CPU instruction cycles. With ScaleFlux, they can compress and decompress at PCIe bus speeds.

Most storage controllers that offer compression/decompression must have some sort of LBA (logical block address) virtualization. Because while the host may be writing 512 or 4096 byte blocks, what’s actually written to the NAND is more like, 231 or 1999 bytes. So packing these odd, variable length blocks into NAND blocks can become a problem. But most SSDs already have a flash translation layer (FTL) where LBA addresses are mapped, over time, to different physical NAND page/block addresses. ScaleFlux has combined support for LBA virtualization and FTL into the same process and by doing so, they reduce IO overhead to perform better.

ScaleFlux’s drive is an NVMe SSD, which already supports great native response times but when you are transferring 1/2 or less of (compressed) data from the host onto NAND, you can reduce latencies even more. .

Although their current generation product is based on TLC NAND they are working on the next generation which will support QLC. And the benefits of writing and reading less data should also help QLC endurance and performance.

Although ScaleFlux is seeing great adoption with just outboard transparent compression and encryption, there is more that could be done, For example,

  • Filtering query’s at the drive rather than at the host. If customers can send a search key/phrase or other filtering request directly to the drive, the drive can pass over all it’s data and send back just the data that matches that filter request.
  • Transcoding and other data format changes. Although transcoding makes a lot of sense to do outboard, Tong also mentioned format changes. We asked him to clarify and he said consider a row based database that needs to be accessed in columnar format. If the drive could change the format from one to the other, it opens up more analytics tool sets.

At the moment, ScaleFlux engineering teams are the ones that program the FPGA to perform outboard functionality. But in a future release, they plan to adding ARM cores in a SoC, which can handle more general purpose outboard functionality as code.

Because of this added complexity of compression, encryption and other outboard logic, we asked Tong what power loss protection was available at the drive level. Tong assured us that once data has been received by their device, it is maintained across a power failure with CAPs and other logic to offload it.

Tong also mentioned that Intel, AWS and the NVMe standard committee are looking at adding some computational storage support into the NVMe standard, so applications and host software can invoke and maybe modify outboard functionality on the fly. Sort of like loading containers of functionality to run on the fly on an SSD drive.

Dr. Tong Zhang, Chief Scientist and Co-fonder, ScaleFlux

Dr. Tong Zhang is a well-established researcher with significant contributions to data storage systems and VLSI signal processing. Dr. Zhang is responsible for developing key techniques and algorithms for ScaleFlux’s Computational Storage products and exploring their use in mainstream application domains.

He is currently a Professor at Rensselaer Polytechnic Institute (RPI). His current and past research span over database, filesystem, solid-state and magnetic data storage devices and systems, digital signal processing and communication, error correction coding, VLSI architectures, and computer architecture.

He has published over 150 technical papers at prestigious USENIX/IEEE/ACM conferences and journals with the citation h-index of 36, and has served as general and technical program chairs for several premier conferences. Among his many research accomplishments, he made pioneering contributions to establishing flash memory signal processing and enabling practical implementation of low-density parity-check (LDPC) codecs. He received two best paper awards and has over 20 issued/pending US patent applications.

He holds BS/MS degrees in EE from the Xi’an Jiaotong University, China, and PhD degree in ECE from the University of Minnesota.

113: GreyBeards talk storage for next gen. workloads with Liran Zvibel, Co-Founder & CEO WekaIO

Sponsored By:

I’ve known Liran Zvibel, Co-founder and CEO of Weka IO for many years now and it’s the second time he’s been on our show, (see: Episode 56: GreyBeards talk high performance file storage...). In those days, WekaIO was just coming out and hitting the world with this extremely high-performing, scale out unstructured data solution. Well since then, they’ve just gotten better.

Keith and I had a great time talking with Liran again. Liran has deep knowledge about unstructured data and how enterprises use it these days. WekaIO’s story, over the last two years has gone beyond great performance to real world, hybrid cloud offerings e as well as going after the cloud native app’s (read Kubernetes [K8S]) persistent storage. Listen to the podcast to learn more.

We started with a history lesson on WekaIO. Back in those days (which persists today, I might add) there were many IO workloads that required companies to purchase different solutions for different work. For example, they needed DAS or SAN for performance, NAS for ease of access and object for scale. WekaIO came out with an answer to all these problems in a single, scaleable storage system. That is, they performed IO as fast as DAS or SAN block, had all the ease of access of NAS, and could scale as much as object.

However, the real culprit holding the world back was “NFS”. At the outset NFS was designed (back in the 1990s) with the then current networking speeds available (10-100Mbps), which performed just fine at those speeds. But when 10-100GbE came out in the 2000’s, NFS’s metadata overhead was too chatty to support wire speeds. Thus, any storage that depended on NFS protocols couldn’t supply (small) files fast enough for modern applications.

This is why WekaIO has moved to not only support NFS and SMB but also POSIX and NVIDIA® GPUDirect® Storage interfaces. By offering POSIX, WekaIO is able to plug into standard Linux and Windows server systems and provide excellent small file performance. Of course applications that demand small file performance today are mostly data analytics and AI/ML/DL workloads.

Consequently., NVIDIA came out with their GPUDirect Storage protocol to address getting small file (data) into GPUs faster. With GPUDirect, storage systems can RDMA data directly from storage to GPU memory and vice versa, with no OS intervention (other than to set up the transfer). If you happen to have a small file, high performing storage system attached to your fabric that supports GPUDirect , like WekaIO, you can significantly speed up your AI/ML/DL workloads.

Next we started talking K8S storage. WekaIO usestheir POSIX interface in their CSI plugin to support K8S container persistent storage. Again, supplying high performance for small files seems to be tailor made for K8S container applications that exist today and will for the foreseeable future.

Enter the cloud. Almong other things, WekaIO is a AWS primary storage vendor. It also offers snap to cloud. And with both of these in tandem, it’s just become a lot easier to move and access your unstructured data in the cloud. Liran mentioned that WekaIO primary storage in AWS operates across AZ’s. This means it can be configured to support better availability than EBS.

Large BioPharma companies are using WekaIO in AWS to store and process field data and research data, so that this work can be done around the world. Some companies have run out of compute in a single AZ (unbelievable I know but it’s COVID). By offering multi-AZ support unstructured data access with WekaIO, these companies can spread their compute across AZ’s and region and still access their data. And when their products are ready for gov’t certification, having all this data in the cloud, can make provide an easy way to have gov’t access this same data.

Liran Zvibel, Co-founder and CEO WekaIO

As Co-Founder and CEO, Mr. Liran Zvibel guides long term vision and strategy at WekaIO. Prior to creating the opportunity at WekaIO, he ran engineering at social startup and Fortune 100 organizations including Fusic, where he managed product definition, design, and development for a portfolio of rich social media applications.

Liran also held principal architectural responsibilities for the hardware platform, clustering infrastructure and overall systems integration for XIV Storage System, acquired by IBM in 2007.

Mr. Zvibel holds a BSc.in Mathematics and Computer Science from Tel Aviv University.

110: GreyBeards talk FMS2020 wrap up with Jim Handy, General Director of Objective Analysis

This months it’s back to storage and our annual wrap-up on the Flash Memory Summit Conference with Jim Handy, General Director of Objective Analysis. Jim’s been on our show 5 times before and is a well known expert on NAND and SSDs (as well as DRAM and memory systems). Jim also blogs at TheSSDGuy.com and TheMemoryGuy.com just in case you want to learn more.

FMS went virtual this year and had many interesting topics including how computational storage is making headway in the cloud, 3D QLC is hitting the enterprise with PLC on the way, and for a first at FMS, a talk on DNA storage (for more information on this, see our podcast with CatalogDNA). Jim’s always interesting to talk with to help us understand where the NAND-SSD industry is headed. Listen to the podcast to learn more.

Jim mentioned that the major NAND vendors are all increasing the number of layers for their 3D NAND, and it continues to scale well. Most vendors are currently shipping ~100 layer NAND, with Micron doing more than that. And vendor roadmaps are looking at the possibility of 200 layers or more. Jim doesn’t think anyone knows how high it can go.

Another advantage of 3D NAND is it can be used to make bigger bit cells and thus have better endurance. From Jim’s perspective more electrons per cell means a better more resilient bit cell.

Many vendors in the nascent persistent memory industry were all hoping that NAND would stop scaling at some point and they would be able to pick up the slack. But NAND manufacturers found 3D and scaling hasn’t stopped at all. This has relegated most persistent memory vendors to a small niche market with the exception of Intel (and Micron).

Jim said that Intel is losing money on Optane every year, ~$5B so far. But Intel knows that chip profitability is tied to economies of scale, volumes matter. With enough volume, Optane will become cheap enough to manufacture that they will make buckets of money from it.

Interestingly, Jim said that DRAM scaling is slowing down. That means there may be an even bigger market for something close to DRAM access speeds, but with increased density and lower cost. Optane seems to fit that description very well.

Jim also mentioned that computational storage is starting to see some traction with public cloud vendors. Computational storage adds generic compute power to inside an SSD which can be used to perform storage intensive functions out at the SSD rather than transferring data into the CPU for processing. This makes sense where a lot of data would need to be transferred back and forth to an SSD and where computational cycles are just as cheap out on the SSD as in the server. For example, for data compression, search, and video transcoding, computational storage can make a lot of sense. (See our podcast with NGD systems for more informaiton).

In contrast, Open-Channel SSDs are making dumb SSDs, or SSDs without any flash translation layer or other smarts needed to make NAND work as persistent storage bin the enterprise. There’s a small group of system providers that want to perform all this functionality at a global scale (or across multiple SSDs) rather than at the local, SSD drive level.

Another topic that hit it’s stride this year at FMS2020 was Zoned Name Spaces (ZNS). ZNS partitions an SSD into separately addressable segments, to allow higher performing sequential (write) access within those zones. As SSD capacity has increased, IO activity has sky-rocketed and this has led to an “IO blender” effect. Within an IO blender, it’s impossible to understand which IO is following a sequential pattern and which is not. ZNS is intended to solve that probplem

With ZNS SSDs, IOs doing sequential access can have their own partition and that way the SSD can understand its sequential IO and act accordingly. It turns out that sequential writes to NAND can perform much, much faster than random writes.

ZNS was invented for SMR (shingled magnetic recording) disks, because these overwrote portions of other tracks (like roof shingles, tracks on SMR disks overlap). We had heard about ZNS at FMS2019 but had thought this just a better way to share access to a single SSD, by carving it up into logical (mini-)volumes. Jim said that was also a benefit but the major advantage is being able to understand sequential IO and write to the SSD more effectively.

We talked some on the economics of NAND flash, disk and tape as storage media. Jim and I see this continuing a trend that’s been going on for years, where NAND storage cost $/GB ~10X more than disk capacity, and disk storage costs $/GB ~10X more than tape capacity. All three technologies continue their relentless pursuit of increasing capacity but it’s almost like train tracks, all three $/GB curves following one another into the future.

On the other hand, high RPM disk seems to have died, and been replaced with SSDs. Disk manufacturers have seen unit declines but the # GB they are shipping continues to increase. Contrary to a number of AFA system providers, disk is not dead and is unlikely to die anytime soon.

Finally, we discussed DNA storage and it’s coming entry into the storage market. It’s all a question of price of the drive and media technology, size of the mechanism (drive?) and read and write access times. At the moment all these are coming down but are not yet competitive with tape. But given DNA technology trends, there doesn’t appear to be any physical barrier that’s going to stop it from becoming yet another storage technology in the enterprise, most likely at a 10X $/GB cost advantage over tape…

Jim Handy, General Director, Objective Analysis

Jim Handy of Objective Analysis has over 35 years in the electronics industry including 20 years as a leading semiconductor and SSD industry analyst. Early in his career he held marketing and design positions at leading semiconductor suppliers including Intel, National Semiconductor, and Infineon.

A frequent presenter at trade shows, Mr. Handy is known for his technical depth, accurate forecasts, widespread industry presence and volume of publication.

He has written hundreds of market reports, articles for trade journals, and white papers, and is frequently interviewed and quoted in the electronics trade press and other media. 

He posts blogs at www.TheMemoryGuy.com, and www.TheSSDguy.com

106: Greybeards talk Intel’s new HPC file system with Kelsey Prantis, Senior Software Eng. Manager, Intel

We had talked with Intel at Storage Field Day 20 (SFD20), about a month ago. At the virtual event, Intel’s focus was on their Optane PMEM (persistent memory) technology. Kelsey Prantis (@kelseyprantis), Senior Software Engineering Manager, Intel was on the show and gave an introduction into Intel’s DAOS (Distributed Architecture Object Storage, DAOS.io) a new HPC (high performance computing, super computers) file system they developed from scratch to use leading edge, Intel technologies, and Optane PMEM was one of them.

Kelsey has worked on LUSTRE and other HPC file systems for a long time now and came into the company from the acquisition of Whamcloud. Currently, she manages the development team working on DAOS. DAOS is a new HPC object storage file system which is completely open source (available on GitHub).

DAOS was designed from the start to take advantage of NVMe SSDs and Optane PMEM. With PMEM, current servers can support up to 20TB of memory. Besides the large memory sizes, Optane PMEM also offers non-volatile memory and byte addressability (just like DRAM). These two characteristics opens up new functionality that allows DAOS to move beyond legacy, block oriented, storage architectures that have been the only storage solution for HPC (and the enterprise) for decades now.

What’s different about DAOS

DAOS uses PMEM for all metadata and for storing small files. HPC IO has always focused on heavy bandwidth (IO using large blocks) oriented but lately newer applications have emerged, such as AI/ML/DL, data analytics and others, that use smaller files/blocks. Indeed, most new HPC clusters and supercomputers are deploying almost as many GPUs as CPUs in their configurations to support AI activities.

The problem is that these newer applications typically consume much smaller files. Matt mentioned one HPC client he worked with was processing small batches of seismic data, to predict, in real time, earthquakes that were happening around the world.

By using PMEM for metadata and small files, DAOS can be much more responsive to file requests (open, close, delete, status) as well as provide higher performing IO for small files. All this leads to a much better performing system for the new HPC workloads as well as great sustainable performance for the more traditional large file workloads.

DAOS storage

DAOS provides a cluster storage system that can be configured with from 1 (no data protection), but more normally 3 nodes (with data protection) at a minimum to 512 nodes (lab tested). Data protection in DAOS is currently based on mirroring data and can use from 0 to the number of nodes in a cluster as data mirrors.

DAOS system nodes are homogeneous. That is they all come with the same amount of PMEM and NVMe SSDs. Note, DAOS doesn’t support disk drives. Kelsey mentioned DAOS node hardware can be tailored to suit any particular application environment. But they typically require an average of 6% of overall DAOS system capacity in PMEM for metadata and small file activity.

DAOS current supports their own API, POSIX, HDFS5, MPIIO and Apache Spark storage protocols. Kelsey mentioned that standard POSIX uses a pessimistic conflict resolution mode which leads to performance bottlenecks during parallel access. In contrast, DAOS’s versos of POSIX uses optimistic conflict resolution, which means DAOS starts writes assuming there’s no conflict, but if one occurs it handles the conflict in real time. Of course with all the metadata byte addressable and in PMEM this doesn’t take up a lot of (IO) time.

As mentioned earlier, DAOS data protection uses mirror-replicas. However, unlike most other major file systems, DAOS mirroring can be done at the object level. DAOS internally is an object store. Data organization on DAOS starts at the pool level, underneath that is data containers, and then under that are objects. Any object in DAOS can have its own mirroring configuration. DAOS is working towards supporting Erasure Coding as another form of data protection for a future release.

DAOS performance

There’s a new storage benchmark that was developed specifically for HPC, called the IO500. The IO500 benchmark simulates a number of different HPC workloads, measures performance for each of them, and computes an (aggregate) performance score to rank HPC storage systems.

IO500 ranks system performance using two lists: one is for any sized configuration that typically range from 50 to 1000s of nodes and their other list limits the configuration to 10 nodes. The first performance ranking can sometimes be gamed by throwing more hardware into a cluster. The 10 node rankings are much harder to game this way and from our perspective, show a fairer comparison of system performance.

As presented (virtually) at ISC 2020, DAOS took the top spot on the IO500 any size configuration list and performed better than 2X the next best solution. And on the IO500 10 node list, Intel’s DAOS configuration, Texas Advanced Computing (TAC) DAOS configuration, and Argonne Nat Labs DAOS configuration took the top 3 spots and had 3X better performance than the next best, non-DAOS storage system.

The Argonne National Labs has already stated that they will be using DAOS in their new HPC system to be deployed in the near future. Early specifications for storage at the new Argonne Lab required support for 230PB of data and 25TB/sec of bandwidth.

The podcast ran ~43 minutes. Kelsey was great to talk with and very knowledgeable about HPC systems and HPC IO in particular. Matt has worked at Argonne in the past so understood these systems better than I. Sadly, we lost Matt’s end of the conversation about 1/2 way into the recording. Both Matt and I thought that DAOS represents the birth of a new generation of HPC storage. Listen to the podcast to learn more.


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Kelsey Prantis, Senior Software Engineering Manager, Intel

 Kelsey Prantis heads the Extreme Storage Architecture and Development division at Intel Corporation. She leads the development of Distributed Asynchronous Object Storage (DAOS), an open-source, low-latency and high IOPS object store designed from the ground up for massively distributed Non-Volatile Memory (NVM).

She joined Intel in 2012 with the acquisition of Whamcloud, where she led the development of the Intel Manager for Lustre* product.

Prior to Whamcloud, she was a software developer at personal genomics and biotechnology company 23andMe.

Prantis holds a Bachelor’s degree in Computer Science from Rochester Institute of Technology

104: GreyBeards talk new cloud defined (shared) storage with Siamak Nazari, CEO Nebulon

Ray has known Siamak Nazari (@NebulonInc), CEO Nebulon for three companies now but has rarely had a one (two) on one discussion with him. With Nebulon just emerging from stealth (a gutsy move during the pandemic), the GreyBeards felt it was a good time to get Siamak on the show to tell us what he’s been up to. Turns out he and Nebulon decided it was time to completely rethink/rearchitect shared storage for the new data center.

At his prior company, Siamak spent a lot of time with many customers discussing the problems they had dealing with the complexity of managing, provisioning and maintaining multiple shared storage arrays. Somewhere in all those discussions Siamak saw this as a problem that needed a radical solution. If we could just redo shared storage from the ground up, there might be a solution to all these problems.

Redefining shared storage

Nebulon’s new approach to shared storage starts with an SPU card which replaces SAS RAID cards in a server. But instead of creating SAS RAID groups, the SPU creates a shareable, enterprise class, pool of storage across a throng of servers.

They call a collection of servers with SPUs, Cloud Defined Storage (CDS) and it creates a Nebulon nPod. An nPod essentially consists of multiple servers with SPU cards, with or without attached SSD storage, that are provisioned, managed and monitored via the cloud. Nebulon nPod servers are elements or nodes of a shared storage pool across all interconnected SPU servers in a data center.

In an SPU server with local (SAS, SATA, NVMe) SSD storage, the SPU creates an erasure coded pool of storage which can be used to serve (SAS) LUNs to this or any other SPU attached server in the nPod. In a SPU server without local SSD storage, the SPU provides access to any other SPU server shared storage in the nPod. Nebulon nPods only works with flash storage, it doesn’t support spinning media.

The SPU can supply boot storage for its server. There’s no need to have the CPU running OS code to use nPod shared storage. Yes, the SPU needs power and an active PCIe bus to work, but the functionality of an SPU doesn’t require an operational OS to work. The SPU provides a SAS LUN interface to server CPUs.

Each SPU has dual port access to an inter-cluster (25GbE) interconnect that connects all SPUs to the nPod. The nPod inter-cluster protocol is proprietary but takes advantage of standard TCP/IP services across the network with standard 25GbE switching.

The SPU firmware insures that it stays connected as long as power is available to the server. Customers can have more than one SPU in a server but these would be used for more IO performance. Each SPU also has 32GB of NVRAM for caching purposes and it’s also used for power fail fault tolerance.

In the unlikely case that the server and SPU are completely down (e.g. power outage), clients can still access that SPUs data storage, if it was mirrored (see below). When the SPU server comes back up, it will be resynched with any data that had been changed.

Other Nebulon storage features

Nebulon supports data-at-rest encryption, compression and deduplication for customer data. That way customer data is never in plain text as it travels across the nPod or even within the server from the SPU to SSD storage. Also any customer data written to an nPod can be optionally mirrored and as noted above, is protected via erasure coding.

The SPU also supports snapshotting of customer LUN data. So clients can take copies of LUNs and use these for backups, test, dev, etc. SPUs also support asynchronous or synchronous replication between nPods. For synchronous replication and mirrored data, the originating host only sees the IO complete after the data has been received at the target SPU or nPod.

Metadata for the nPod that defines LUN configurations and which server has LUN data is kept across the cluster in each SPU. But metadata on the location of user data within a server is only kept in that server’s SPU.

We asked Siamak whether nPods support SCM (storage class memory). He said not yet, but they’re looking at SCM NVMe storage for use as a potential metadata and data cache for SPUs.

Nebulon Application Centric storage

All the above storage features are present in most enterprise class storage systems. But what sets Nebulon apart from all other shared storage arrays is that their control plane is entirely in the cloud. That is customers point their browser to Nebulon’s control plane and use it to configure, provision and manage the nPod storage pool. Nebulon supports application templates that can be used to configure nPod storage to support standardized applications, such as VMware VMs, MongoDB, persistent storage for K8S containers, bare metal Linux apps, etc.

With the nPod’s control plane in the cloud it makes provisioning, managing and monitoring storage services much more agile. Nebulon can literally roll out new control plane updatesy to their install base on an almost daily basis. Just like any other cloud based or SAAS application. Customers receive the updated nPod control plane functionality by simply refreshing their browser page.

Nebulon’s GoToMarket

Near the end of our podcast, we asked Siamak about how Nebulon was going to access the market. Nebulon’s goto market is to use server OEMs. That is, they have signed agreements with two (and working on a third) server vendors to sell SPU cards with Nebulon control plane access.

During server purchases, customers configure their servers but now along with SAS RAID card options they will now see an Nebulon SPU option. OEM server vendors will bundle SPU hardware and Nebulon control plane access along with all other server components such as CPU’s, SSDs, NICs, etc, This way, the customer will receive a pre-installed SPU card in their server and will be ready to configure nPod LUNs as soon as the server powers on in their network.

Nebulon will go GA in the 3rd quarter.

The podcast ran ~43 minutes. Siamak has always been a pleasure to talk with and is very knowledgeable about the problems customers have in today’s data center environments. Nebulon has given him and his team the way to rethink storage and address these serious issues. Matt and I had a good time talking with Siamak. Listen to the podcast to learn more.

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Siamak Nazari, CEO Nebulon

Siamak Nazari is the CEO and Co-founder of Nebulon. Siamak has over 25 years of experience working on distributed and highly available systems.

In his position as HPE Fellow and VP, he was responsible for setting technical direction for HPE 3PAR and its portfolio of software and hardware. He worked on HPE 3PAR technology from 2000 to 2018, responsible for designing and implementing distributed memory management and the high availability features of the system.

Prior to joining 3PAR, Siamak was the technical lead for distributed highly available Proxy Filesystem (pxfs) of Sun Cluster 3.0.