Berkeley Releases Cloud Computing Study

By Nicole Hemsoth

February 12, 2009

Researchers at the Reliable Adaptive Distributed Systems Laboratory (RAD Lab) at UC Berkeley have released a 23-page white paper, Above the Clouds [PDF], that provides an in-depth analysis of the emerging cloud computing model. The paper is one of the first academic treatises on the subject to offer a critical profile of the cloud computing landscape today.

We asked two of the paper’s authors, David Patterson, Professor in Computer Science at UC Berkeley, and Armando Fox, Adjunct Associate Professor at UC Berkeley’s RAD Lab, to elaborate on the findings and offer their perspective on how the cloud will impact high performance computing.

HPCwire: Cloud computing has come to mean a variety of things. For the purpose of our discussion here, how would you define it?

David Patterson: Cloud computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a cloud. When a cloud is made available in a pay-as-you-go manner to the general public, we call it a “public cloud”; the service being sold is utility computing. We use the term “private cloud” to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, cloud computing is the sum of SaaS and utility computing, but does not include private clouds.

We don’t use terms such as “X as a service” (XaaS); values of X we have seen include infrastructure, hardware, and platform, but we were unable to agree, even among ourselves, what the precise differences among them might be.

Armando Fox: The key ingredient is having tremendous computing resources instantly available on-tap with no advance arrangements needed and pay-as-you-go billing. Especially relevant is the fact that once you release unused resources, you don’t have to pay for them anymore. This property of “elasticity” shifts many risks from the users of that equipment to the provider of the equipment, creating new economic models that can change the way that startups, researchers, and even established enterprises think about IT spending.

HPCwire: Cloud computing is arguably the biggest paradigm shift in IT since the PC. Although similar concepts like utility computing and grid computing have been around for some time, they never attained widespread commercial success. What pieces of technology have come together to make cloud computing viable today?

Fox: While there are many technical factors, we believe the most important is the existence of extremely large datacenters built from tens of thousands of commodity computers. It turns out also that there are cost advantages of a factor of five to seven in capitalizing a datacenter at this scale compared to, say, a medium-sized enterprise datacenter of hundreds of computers. And the huge growth of the Internet drove companies such as Google, Amazon, eBay, and others to build such datacenters, to develop infrastructure software for them, such as Google File System or Amazon Dynamo, and to develop the operational expertise to armor them against the hostile environment of the public Internet.

Patterson: These technical advances were matched by a business model that offers three key features: 1) The illusion of infinite computing resources available on demand; 2) The elimination of an up-front commitment by cloud users, thereby allowing companies to start small; and 3) The ability to pay for use of computing resources on a short-term basis as needed and to release them when unneeded. Past efforts at utility computing failed because one or two of these three critical characteristics were missing. For example, Intel Computing Services in 2000-2001 required negotiating a contract and longer-term use than per hour.

Alas, grid computing created protocols that offered shared computation and storage over long distances and did not lead to a software environment that grew beyond the HPC community.

HPCwire: There are some prominent people in the industry like Richard Stallman — quoted in the paper — who portray cloud services as marketing hype and who are wary of becoming dependent on cloud and service providers. Is this just resistance to new paradigms or do people like Stallman have a valid point?

Fox: While we believe that cloud computing is definitely more than just “marketing hype,” we agree that the uncertainty of having one’s data and applications “locked in the cloud” may be a potential obstacle to cloud adoption. As we describe in the paper, cloud offerings may differ in the level of management and functionality offered in the cloud. For example, Amazon’s offering relies heavily on the appeal of a robust open-source software ecosystem and provides relatively little in the way of “built-in” functionality; whereas, Microsoft Azure allows deployed applications to run in a managed .NET environment and make use of the .NET framework and libraries, making those applications (and potentially, the data they manage) more difficult to move to another cloud provider that might not offer .NET.

Patterson: We think there is a potential danger to business continuity if you are dependent on a single cloud computing provider. We argue that such concerns can be addressed by standardizing APIs so that multiple providers can offer the same service, so that cloud computing users can move their application if a provider offers poor service or goes out of business.

The obvious fear is that this would lead to a “race-to-the-bottom” and would flatten the profits of cloud computing providers. We offer two arguments to allay this fear. First, the quality of a service matters as well as the price, so customers will not necessarily jump to the lowest cost service. Some Internet service providers today cost a factor of ten more than others because they are more dependable and offer extra services to improve usability. Second, standardization of APIs enables a new usage model in which the same software infrastructure can be used in a local datacenter and in a public cloud. Such an option could enable “surge computing,” in which the public cloud is used to capture the extra tasks that cannot be easily run in the datacenter (or private cloud) due to temporarily heavy workloads. We think surge computing could significantly expand the size of the cloud computing market.

HPCwire: The paper lists ten obstacles to cloud computing. Can you point to one or two that seem the most important overall, and also for high performance computing in particular?

Fox: It was really hard to rank-order these, and even the order in the paper is only a partial order. But we all agreed that cloud computing needs standardized APIs that would work across cloud vendors. This would help address TWO obstacles, namely maintaining high availability and preventing data lock-in. As far as technical obstacles, we observed that just as in the past, the cost of long-haul network bandwidth is falling more slowly than all other hardware costs, so we would like to see novel ways that cloud providers could address this high cost of data transfer, such as allowing customers to FedEx a box of disks directly to the cloud datacenter.

For HPC, we think some basic software infrastructure, such as gang scheduling for clouds, would help a lot; but in general, the HPC community has not had to go through the process of re-architecting software that the Web community went through in the 90s. We think there are plenty of opportunities for innovation if HPC steps up to the plate, and an early demonstration would go a long way toward jump starting that area. We’re discussing some possibilities at the Berkeley Par Lab, just upstairs from the RAD Lab.

HPCwire: The paper also describes some new application opportunities. Can you outline these and talk about why they are particularly suitable for cloud computing?

Fox: A major new area is allowing desktop apps to extend seamlessly into the cloud; for example, the popular analysis software MATLAB and Mathematica both support this now. Also, because of the “cost associativity” of the cloud — using 1,000 computers for an hour is the same price as one computer for 1,000 hours — it is great for apps that parallelize well, like document conversion, photo or video rendering, and so on. Of course, because of the relatively high cost of data transfer, the key is applications for which a lot of computing can be done on each byte transferred into the cloud — an observation made by Jim Gray in 2003 — and for which the latency to transfer that data is small compared to the time during which the data will remain “useful” in the cloud.

We also see the cloud supporting surge computing, where a private datacenter can temporarily overflow into a public cloud to support unexpected surges in workload.

HPCwire: Where do you think cloud computing will fit into the HPC application space?

Patterson: If technical issues like gang scheduling of VMs and higher network bandwidth within the datacenter are addressed, we think many users of HPC applications would love to take advantage of the cloud’s new cost associativity: no extra charge for using 20 times as many computers to get your results back in 1/20th the time. We’re conditioned to buying a set of computers and then trying to keep them uniformly busy. This elasticity of resources, without paying a premium for large scale, is unprecedented, so it will take a while for clever people to exploit this opportunity.

When HPC users don’t have to pay the costs of operating their computers — someone else pays for the building space, electricity, air conditioning, and so — they may conclude that on average they can get their work done for less than commercial cloud computing, but that seems more like bad accounting than good science.

HPCwire: How does future hardware and software need to be built to take advantage of the cloud model?

Fox: For software, one key approach is focusing on horizontal scalability — the ability to accommodate more users by adding more servers. At the level of storage systems and databases, this remains elusive, as evidenced by the various offerings such as Google AppEngine’s MegaStore, Amazon’s S3 and SimpleDB, and other scalable storage services. Also, to take advantage of elasticity means that software must automatically be able to adapt to unexpected workload changes, machine failures, and eventually, even whole-datacenter outages. Looking at the spectrum of clouds today, Amazon doesn’t provide any built-in service like this (though third parties such as RightScale are stepping in to fill that gap) but allows the developer to architect anything he wants; whereas, Google AppEngine severely constrains the software architecture of your app, but in return you get a lot of that automatic management for free.

Patterson: Hardware systems should be designed at the scale of a container (at least a dozen racks), which will be the minimum purchase size. Cost of operation will match performance and cost of purchase in importance, rewarding energy proportionality, which puts idle portions of the memory, disk and network into low power mode. Processors should work well with VMs; flash memory should be added to the memory hierarchy; and LAN switches and WAN routers must improve in bandwidth and cost.

—–

For more discussion of Berkeley’s cloud computing research, go to the Above the Clouds Web site.

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!

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Weekly Twitter Roundup (Feb. 16, 2017)

February 16, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Cray Posts Best-Ever Quarter, Visibility Still Limited

February 10, 2017

On its Wednesday earnings call, Cray announced the largest revenue quarter in the company’s history and the second-highest revenue year. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Leading Solution Providers

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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