Category: Matt Leib

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120: GreyBeards talk CEPH storage with Phil Straw, Co-Founder & CEO, SoftIron

GreyBeards talk universal CEPH storage solutions with Phil Straw (@SoftIronCEO), CEO of SoftIron. Phil’s been around IT and electronics technology for a long time and has gone from scuba diving electronics, to DARPA/DOD researcher, to networking, and is now doing storage. He’s also their former CTO and co-founder of the company. SoftIron make hardware storage appliances for CEPH, an open source, software defined storage system.

CEPH storage includes file (CEPHFS, POSIX), object (S3) and block (RBD, RADOS block device, Kernel/librbd) services and has been out since 2006. CEPH storage also offers redundancy, mirroring, encryption, thin provisioning, snapshots, and a host of other storage options. CEPH is available as an open source solution, downloadable at CEPH.io, but it’s also offered as a licensed option from RedHat, SUSE and others. For SoftIron, it’s bundled into their HyperDrive storage appliances. Listen to the podcast to learn more.

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SoftIron uses the open source version of CEPH and incorporates this into their own, HyperDrive storage appliances, purpose built to support CEPH storage.

There are two challenges to using open source solutions:

  • Support is generally non-existent. Yes, the open source community behind the (CEPH) project supplies bug fixes and can possibly answer some questions but this is not considered enterprise support where customers require 7x24x365 support for a product
  • Useability is typically abysmal. Yes, open source systems can do anything that anyone could possibly want (if not, code it yourself), but trying to figure out how to use any of that often requires a PHD or two.

SoftIron has taken both of these on to offer a CEPH commercial product offering.

Take support, SoftIron offers enterprise level support that customers can contract for on their own, even if they don’t use SoftIron hardware. Phil said the would often get kudos for their expert support of CEPH and have often been requested to offer this as a standalone CEPH service. Needless to say their support of SoftIron appliances is also excellent.

As for ease of operations, SoftIron makes the HyperDrive Storage Manager appliance, which offers a standalone GUI, that takes the PHD out of managing CEPH. Anything one can do with the CEPH CLI can be done with SoftIron’s Storage Manager. It’s also a very popular offering with SoftIron customers. Similar to SoftIron’s CEPH support above, customers are requesting that their Storage Manager be offered as a standalone solution for CEPH users as well.

HyperDrive hardware appliances are storage media boxes that offer extremely low-power storage for CEPH. Their appliances range from high density (120TB/1U) to high performance NVMe SSDs (26TB/1U) to just about everything in between. On their website, I count 8 different storage appliance offerings with various spinning disk, hybrid (disk-SSD), SATA and NVMe SSDs (SSD only) systems.

SoftIron designs, develops and manufacturers all their own appliance hardware. Manufacturing is entirely in the US and design and development takes place in the US and Europe only. This provides a secure provenance for HyperDrive appliances that other storage companies can only dream about. Defense, intelligence and other security conscious organizations/industries are increasingly concerned about where electronic systems come from and want assurances that there are no security compromises inside them. SoftIron puts this concern to rest.

Yes they use CPUs, DRAMs and other standardized chips as well as storage media manufactured by others, but SoftIron has have gone out of their way to source all of these other parts and media from secure, trusted suppliers.

All other major storage companies use storage servers, shelves and media that come from anywhere, usually sourced from manufacturers anywhere in the world.

Moreover, such off the shelf hardware usually comes with added hardware that increases cost and complexity, such as graphics memory/interfaces, Cables, over configured power supplies, etc., but aren’t required for storage. Phil mentioned that each HyperDrive appliance has been reduced to just what’s required to support their CEPH storage appliance.

Each appliance has 6Tbps network that connects all the components, which means no cabling in the box. Also, each storage appliance has CPUs matched to its performance requirements, for low performance appliances – ARM cores, for high performance appliances – AMD EPYC CPUs. All HyperDrive appliances support wire speed IO, i.e, if a box is configured to support 1GbE or 100GbE, it transfers data at that speed, across all ports connected to it.

Because of their minimalist hardware design approach, HyperDrive appliances run much cooler and use less power than other storage appliances. They only consume 100W or 200W for high performance storage per appliance, where most other storage systems come in at around 1500W or more.

In fact, SoftIron HyperDrive boxes run so cold, that they don’t need fans for CPUs, they just redirect air flom from storage media over CPUs. And running colder, improves reliability of disk and SSD drives. Phil said they are seeing field results that are 2X better reliability than the drives normally see in the field.

They also offer a HyperDrive Storage Router that provides a NFS/SMB/iSCSI gateway to CEPH. With their Storage Router, customers using VMware, HyperV and other systems that depend on NFS/SMB/iSCSI for storage can just plug and play with SoftIron CEPH storage. With the Storage Router, the only storage interface HyperDrive appliances can’t support is FC.

Although we didn’t discuss this on the podcast, in addition to HyperDrive CEPH storage appliances, SoftIron also provides HyperCast, transcoding hardware designed for real time transcoding of one or more video streams and HyperSwitch networking hardware, which supplies a secure provenance, SONiC (Software for Open Networking in [the Azure] Cloud) SDN switch for 1GbE up to 100GbE networks.

Standing up PB of (CEPH) storage should always be this easy.

Phil Straw, Co-founder & CEO SoftIron

The technical visionary co-founder behind SoftIron, Phil Straw initially served as the company’s CTO before stepping into the role as CEO.

Previously Phil served as CEO of Heliox Technologies, co-founder and CTO of dotFX, VP of Engineering at Securify and worked in both technical and product roles at both Cisco and 3Com.

Phil holds a degree in Computer Science from UMIST.

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117: GreyBeards talk HPC file systems with Frank Herold, CEO of ThinkParQ, makers of BeeGFS

We return back to our storage thread with a discussion of HPC file systems with Frank Herold, (@BeeGFS) CEO of ThinkParQ GmbH, the makers of BeeGFS. I’ve seen BeeGFS start to show up in some IO500 top storage benchmark results and as more and more data keeps coming online every day, we thought it time to start finding out how our friends in the HPC world handle their data deluge.

Frank’s a former rocket scientist, that’s been in and around the storage industry for years, and was very knowledgeable about BeeGFS’s software defined, parallel file system. He seemed to have a great grasp of the IO requirements in HPC, Life Sciences and other HPC-like applications. Listen to the podcast to learn more.

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Turns out that ThinkParQ is a spinoff of the research institute in Germany that originally developed BeeGFS parallel file system. There are apparently two version of their product one which is publicly available (downloadable from their website) and another with commercial support. It’s not quite 100% open source but it’s got a lot of open source in it and their GIT repository is available

BeeGFS was primarily focused on HPC workloads but as this type of work has become more mainstream, they have moved beyond HPC and now have significant installations in Life Sciences, Oil&Gas and many other big data environments.

It runs on x86/AMD, OpenPower, and ARM CPUs. BeeGFS comes as a number of services, one of which is a storage service which uses a backend with ZFS or XFS file system. It also uses (POSIX compliant) host client software to access their system. There’s also a metadata and monitoring service. Most of the time these services run on separate servers but BeeGFS also supports a “converged mode”, where all these services run on a single server. And you can have multiple converged mode servers in a cluster.

BeeGFS is a parallel file system. This means that it intrinsically supports multiple metadata services/servers and multiple storage servers which allow it to scale up storage bandwidth and performance considerably beyond single appliance systems. Data is automatically distributed across all the storage servers in the configuration, unless you specify that data reside on specific, say all flash storage servers. Similarly, metadata is automatically distributed across all metadata servers in the system.

They don’t support any specific RAID protection other than mirroring and that really to speed up read throughput. Rather they depend on the underlying XFS/ZFS file system to provide drive failure protection (RAID5/6).

One of BeeGFS’s selling points is that it has few tuning parameters that a customer needs to fiddle with. Frank said it runs quite well right out of the box.

BeeGFS offers a single name space that spans the cluster (of metadata servers/storage servers). But customers can elect to split this name space across a subset of these metadata and storage servers, and by doing so they create multiple BeeGFS clusters.

There’s no inherent support for NFS or SMB but customers can configure NFS or SAMBA servers that use BeeGFS as backend storage. Also, there’s no data reduction built into BeeGFS and no automatic data tiering across the backend storage (file systems).

But as noted above, customers can direct which backend storage to use to hold their data. And they do offer a CLI data movement primitive and customers can use this in conjunction with other software to implement storage tiering or do it themselves.

Metadata performance is extremely important for small files and for large multi Billion object file systems. BeeGFS uses extensive metadata caching to provide faster access to this information.

Speaking of small file performance, we had a decent discussion on the tradeoffs involved between small and large file performance. And although BeeGFS has decent small file performance it’s not a be all for every small file intensive application. According to Frank, not every small file workload is optimal for BeeGFS.

They offer BeeOND which is BeeGFS on demand. This is an integration with Slurm workload scheduler (HPC work scheduler) that allows customers to spin up a scratch BeeGFS parallel file system across compute servers with storage.

Slurm’s BeeOND integration brings all BeeGFS services up and deploys them on compute nodes you specify. At this point you have a fully installed BeeGFS (scratch) parallel file system. Customers may use this scratch file system to support any compute-data intensive workload theyneed to run. When no longer needed, Slurm can be directed to automatically dismantle the BeeGFSl file system.

We talked about BeeGFS partners. They have a number of regional partners that provide installation and onsite support and a number of technical partners, such as NetApp, Dell, HPE and INSPUR, that supply BeeGFS configured servers and systems for deployment/installation.

Frank Herold, CEO ThinkparQ

Frank Herold is the CEO of ThinkParQ GmbH – the company behind BeeGFS. He actively leads the company and the product strategy of BeeGFS as a global player for parallel high-performance file systems.

Prior to joining ThinkParQ, he held various senior management positions within ADIC and Quantum Corporation, responsible for market segments within the academic and scientific research, oil and gas, broadcast and video surveillance sectors, focusing on large scale, high-performance and enterprise accounts within EMEA. 

Frank has over 25 years of experience in the IT industry and holds a master’s degree in engineering (Dipl. -Ing.) in rocket science.

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115-GreyBeards talk database acceleration with Moshe Twitto, CTO&Co-founder, Pliops

We seem to be on a computational tangent this year. So we thought it best to talk with Moshe Twitto, CTO and Co-Founder at Pliops (@pliopsltd). We had first seen them at SFD21 (see videos of their sessions here) and their talk on how they could speed up database IO was pretty impressive. Essentially, they have a database/storage accelerator board used to increase block store IO activity to NVMe SSDs but also provide a key-value store IO accelerator,

Moshe was very knowledgeable about the technology and had previously worked at Samsung for their SSD group. He knew a lot about what happens underneath the covers of an SSD and what it takes to speed up IO. It turns out that many in memory databases use persistent key value stores to persist data or to operate in non- (or partial-) memory-mode. Listen to the podcast to learn more.

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The Pliops board plugs into the PCIe bus and accelerates IO to NVMe SSDs connected to the bus or can act to accelerate IO to JBoF that’s networked behind it. Their board uses FPGA(s), NVDimms of their own design and DRAM to accelerate database IO using NVMe SSDS.

Pliops operates in one of two modes, as a Key-Value store or as a Block store. Their Key-Value store takes advantage of block store capabilities, so we start there.

In block mode, Pliops provides inline hardware data compression and encryption. Compression requires support for variable length blocks on backend SSDs. To better support this, they pack multiple compressed blocks into physical blocks. They also use a virtualization service to support mapping host LBAs to physical block addresses (using an internal key-value store). Hardware, inline encryption is also provided on a LUN (or namespace) basis. This could enable each database to have its own key. They have a root-of-trust secret key used to encrypt customer namespace (database) keys.

They also optimize physical block layouton the SSD to reduce write amplification (doing more than one write to the NAND for every host write to the SSD).

Block mode also supports smart caching. This is especially useful for database journaling/loging which reuses a portion of LBA address space (blocks} as a revolving journal/log. These blocks are overwritten with new data often and data written to them need not be destaged to NVMe SSDs as long as it can be maintained in NVDimm storage. At some point it gets destaged but probably only when log activity slows down (if ever) or some timeout occurs.

For their key-value storage accelerator, they have implemented an API that’s similar to RocksDB, a persistent key-value store, which is used as a physical storage backend for Reddis and similar in-memory databases. However, the challenge with RocksDB is that there are lots of tuning knobs/parameters. So getting right takes some work. But all this can be avoided just by using Pliops.

We didn’t talk too much about how their key-value store works. Moshe says they optimize the key structures and key data so that all database keys can be retained in their board’s memory and just by doing that, they can have immediate (1 IO) access to any data block pointed to by those keys.

He did mention that they provide ~the same performance for a database getting 10-25% host cache hit rates using their board as that same database would support with a 80-90% host cache hit rate not using their board. Some of this was shown at SFD21 (so check out the videos above for more performance info)

A couple of other advantages they bring to the table. As they are interposed between the host and the NVMe SSDs they can take advantage of their NVDIMMs and memory to write much wider stripes than the host writes. This allows them to reduce SSD read and write amplification (due to less garbage collection) by writing more full NAND pages. All this also reduces physical host (data) writes/day which can significantly improve SSD endurance.

Somewhere in all that smart caching and data compression, they are able to also decrease response times It turns out that databases that don’t use RocksDB or depend on key-value stores can easily take advantage of all their block store functionality to improve IO performance.

They mostly market their product to hyperscalers and superscalers. His definition of super-scalers was any organization that operates at public cloud levels but is not a public cloud (e.g., big social media companies).

Moshe Twitto, CTO & Co-founder Pliops

Moshe is an expert in advanced data management and coding algorithms. Prior to co-founding Pliops, Moshe served as CTO of Samsung’s SSD Controller Development Center in Israel.

Moshe holds MSEE, BSEE degrees from Technion University, Summa Cum Laude and served in the Unit 8200 Intelligence Division of the Israel Defense Corps.

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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.

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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.

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112: GreyBeards annual year end wrap-up with Keith & Matt

It’s the end of the year, so time for our regular year end wrap up discussion with the GreyBeards. 2020 has been an interesting year to say the least. It started out just fine, then COVID19 showed up and threw a wrench in everyone’s plans and as the year closes, we were just starting to see some semblance of the new normal, when one of the largest security breaches in years shows up. Whew, almost glad that’s over and onto 2021.

As always the GreyBeards had a great discussion on these and other topics to highlight the year just past. The talk was wide ranging and hard to characterize but I did my best below. Listen to the podcast to learn more.

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COVID19s impact on the enterprise

It will probably take some time before we learn the true, long term impacts of COVID19 on IT but one major change has to be the massive Work From Home (WFH) transition that took place overnight.

While WFH can be more productive for some, the lack of face2face interaction can be challenging for others. The fact that many of the GreyBeards have been working from home for decades now, left us a bit oblivious to how jarring this transition can be for newcomers.

There’s definitely some psychological changes that need to occur to be productive at WFH. Organization skills become even more important. Structured interactions (read conference calls, zoom/webex and other forms of communication become much more important. And then there’s security.

Turns out VMware and others have been touting VDI solutions for the past decade or so to better support remote work and at the same time providing corporate levels of security for remote work. While occasionally this doesn’t work quite as well as expected, it’s certainly much much better than having end users access corporate data without any security around that data or worse yet, the “bring your own device”. All these VDI solutions had a field day when WFH happened.

Many workers found they could be more productive at WFH, due the less distractions, no commute time and more flexible hours. What happens when COVID19 is vanquished to all these current WFHers is anyone’s guess.

We thought there might be less need for large office campuses/buildings. But there’s something to be said for more collaboration and random interactions through face2face meetings that can only occur in an office setting with workers present at the same time. Some organizations will take to this new way of work while others will try to dial WFH back to non-existent. Where your organization fits on this spectrum and why, will be telling across a number of dimensions.

The rise of ARM

There’s been a slow but steady improvement in ARM processors over the last almost half century. Nowadays it’s starting to make a place for itself in the enterprise. ARH has always been the goto microprocessor for low power solutions (like smartphones) but nowadays they are being deployed in the cloud and even the enterprise. These can be used as server processors but even outside servers, ARM cores are showing up in hardware accelerators as the brains behind SmartNICs, DPUs, SPUs, etc.

Keith made mention AWS 2nd generation Graviton 64-bit ARM processor EC2 instances. And yes there’s significant cost ( & power) savings that can be had using AWS Graviton ARM instances. So the cloud is starting to adopt them. Somewhere over the past couple of years I heard that VMware was porting ESX to work on ARM cores.

But apparently, it’s not just as simple as dropping an ARM multi-core processor into a server and recompiling your code and away you go. Applications need a certain amount of optimization to run effectively on ARM processors. And the speed up between non-optimized and optimized versions of an application running on ARM cores is significant.

As for SmartNICs and DPUs, these are data networking hardware accelerators that provide real time processing capabilities needed to keep up with higher speed networking, 100GbE and beyond. These DPUs perform deep packet inspection, data compression, encryption and other services all at wire speeds.. Yes you could devote 1 or more X86 cores to do this, but it’s much cheaper (and more effective) to do this outside the CPU core. Moreover, performing this activity at the network entry point to the server means that much of this data doesn’t have to be transferred back and forth through server memory. So not only does it save CPU core cycles but also memory size and memory & PCIe bus bandwidth. We published a recent podcast with Kevin Deierling, NVIDIA Networking discussing DPUs if you want to learn more.

Pat made mention at (virtual) VMworld their plans to port ESX to the DPU. Keith followed up on this and asked some other exec’s at VMware about this and they said VMware will more likely support DPUs as just another hardware accelerator in their cluster. In either case, CPU cycles should be freed up and this should help VMware use X86 cores more efficiently. And perhaps this will help them engage in more CPU constrained environments such as Telcom.

Then there’s computational storage. We have been watching this technology for a couple of years now and it’s seeing some success in being deployed to public cloud environments. They seem to be being used to provide outboard data compression. It’s unclear whether these systems depend on ARM processing or not but my bet is that they do. To learn more about computational storage check out these podcasts, FMS2020 wrap up with Jim Handy and our talk with Scott Shadley on NGD’s computational storage.

System security

At yearend, we are learning of a massive security breach throughout US government IT facilities. All based on what is believed to be a Russian hack to a software package that is embedded in a popular networking tool software solution, SolarWinds. They are calling this a software supply chain hack. Although we are mainly hearing about government agencies being hacked, SolarWinds is also pervasive in the enterprise as well.

There have been many hardware supply chain hacks in the past, where a board supplier used chips or logic that weren’t properly vetted. Over time, hardware suppliers have started to scrutinize their supply chains better and have reduced this risk.

And the US government have been lobbying for the industry to use a security chip with a backdoor or to supply back doors to smartphone encryption capabilities. Luckily, so far, none of these have been implemented by industry.

What Russia has shown us is that this particular hack is not limited to the hardware sphere. Software supply chain risk can’t be ignored anymore.

This means that any software application supplier will need to secure their supply chain or bring it all in house. Which may mean that costs for these packages will go up. It’s possible that using a pure open source supply chain may reduce this risk as well. At least that’s the promise of open source.

We said 2020 was an interesting year and it’s going out with a bang.

Matt Leib (@MBLeib), one of our co-hosts, has been blogging in the storage space for over 10 years, with work experience both on the engineering and presales/product marketing.. His blog is at Virtually Tied to My Desktop and he’s on LinkedIN.

Keith Townsend (@CTOAdvisor) is a IT thought leader who has written articles for many industry publications, interviewed many industry heavyweights, worked with Silicon Valley startups, and engineered cloud infrastructure for large government organizations. Keith is the co-founder of The CTO Advisor, blogs at Virtualized Geek, and can be found on LinkedIN.