The Enemies of Interoperability

By Nicole Hemsoth

February 2, 2011

Each week, new insights from the world of interoperability and cloud standards bubble forth, yet despite the constant chatter, there are still dramatic hurdles that must be overcome before interoperability becomes a reality.

Among some of the voices advocating for a change in how cloud computing standards might evolve into workable, sustaining solutions recently was John Considine, former Director of the Platform Products Group at Sun (which he entered following the company’s acquisition of Pirus Networks). Considine is the founder and CTO behind Boston-based CloudSwitch.

In a recent opinion piece, Considine stated that although cloud’s end users desire genuine interoperability–to be able to find the most suitable cloud offering for their needs without being burdened by the concern that the cloud they choose might lock them in later—the current state of offerings makes this impossible in the near term.

Ideally, if we lived in a world of true interoperability, a customer would be able to pick a cloud provider for a particular workload and then, if she decided to move on or the general needs altered, it would be possible to seamlessly move that workload back to the company datacenter or into any number of other cloud environments—all without a major undertaking, which is what such a shift would likely cause.

In Considine’s view, the true enemies of interoperability are as follows:

  • There are many types of requirements from end users feeding into the cloud definition; customers are looking for architectures in the cloud that match their application configurations, performance requirements, geographic locations, and security concerns.  They want specific infrastructure capabilities (think SANs, network gear, and hypervisors) because these are existing enterprise standards, and look for specific flavors of architecture/topologies/OS that most closely match what they already have.
  • This range of customer requirements creates opportunities for cloud providers to differentiate based on features and services that let them serve specific market segments better than their competitors – think security, performance, specialties (like government or medical), or even different hypervisors (for compatibility with in-house platforms), networking architectures, and pricing models.
  • The competition among cloud providers in turn leads to intense “land grabs” by technology vendors in the cloud market. This includes the big guys like VMware, Microsoft, and Citrix as well as startups like Eucalyptus, Cloud.com, and Nimbula.  It also includes most of the networking players and many of the IT ops providers. Each of these vendors has a different view on how cloud infrastructure should be built and managed (using their solutions and core components), and these differences alter the design of the cloud as well as the attributes of the cloud that the end users can control. 

Considine’s more recent experiences with CloudSwitch provide him with something of a unique point of view in that he works with both enterprises and the cloud providers in bridging the needs of both. He provided some context for one of the largest enemies of interoperability, which is rooted in architecture, stating:

“What we’ve seen is that the cloud providers—the people providing the core infrastructure, at least in the IaaS sphere—have to make choices on their architectures…If someone builds a cloud they have to pick everything from hypervisors, storage, servers, storage networking and the core network, but those choices are always informed by the design center, which dictates who and what market they want to go after and what they want to provide to with their cloud offering.

He gives some context to this idea, noting that in the early days of cloud computing, the architectures were structured according to the dominant needs of the time. Thus, in the case of Amazon, the design center’s goals were to structure clouds around Web 2.0 concepts since customers were driven by web-facing applications and an associated architecture to support them.

“Invariably they were driven toward highly stateless architecture; storage was not very important because the driving factors were really read-only. They were trying to optimize for the public facing website networking capabilities so that architecture became the basis of those clouds…As you can imagine that decision in terms of equipment and architecture was not a good match for backoffice applications or even potentially HPC.

So you see, the cloud providers have to make these decisions and it kind of permeates everything they do; when you start talking about formats for machine images or network options or even applications (in the early days in Amazon there was no persistent storage so if you shut down your instance or it crashed, everything was gone) you see how this is not a good fit for enterprise applications.”

In short, the decisions that cloud providers make dictates the kinds of workloads they can support, but with this differentiation rooted in the design center—the hub of these decisions based on what markets they want to chase—comes even further divergence from any goal of convergence.

“Image formats, storage and how you manage it, pricing and SLA agreements, quality of service—these all have different parameters because the fundamental architectures are different. So instead of cloud providers trying to drive to a common architecture they’re actually driven to an uncommon architecture; an unshared vision of how the cloud should be built.”

Near the end of our chat, we discussed the issue of possible solutions, or at least hopeful signs of progress in the right direction for interoperability. Perhaps not surprisingly, his answer was rather bleak.

He stated that while he doesn’t see anything hopeful on the horizon, there are some interesting projects that do show some sparks of progress in the right direction, including OpenStack, which is the open source collaboration that is being promoted by Rackspace. Again, despite the designation of “open” and NASA involvement, the primary vendor behind the push is one of the world’s largest hosting companies, which does change the nature of the offering to some extent.
 

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “pre-exascale” award), parsed out additional information ab Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid whoops and hollers from the crowd, Thomas Sterling presented t Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out plans to push deeper into climate science and develop more gran Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale companies and their embrace of AI and deep learning – tha Read more…

By Doug Black

HPE Extreme Performance Solutions

Creating a Roadmap for HPC Innovation at ISC 2017

In an era where technological advancements are driving innovation to every sector, and powering major economic and scientific breakthroughs, high performance computing (HPC) is crucial to tackle the challenges of today and tomorrow. Read more…

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network designed to emulate and compete with the human brain. In thi Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big data and artificial intelligence software to its top-of-the-l Read more…

By Alex Woodie

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “global” launch event in Austin TX. In many ways it was a fu Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it, analysts and journalists want to report on it. Deep learni Read more…

By Doug Black

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid wh Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out pla Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big d Read more…

By Alex Woodie

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “g Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it Read more…

By Doug Black

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This