Thursday, February 12, 2009

Above the Clouds Released

We've just released our white paper: "Above the Clouds: A Berkeley View of Cloud Computing."

Executive summary:

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about over-provisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or under-provisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. 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. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing:

  1. The illusion of infinite computing resources available on demand, thereby eliminating the need for Cloud Computing users to plan far ahead for provisioning.
  2. The elimination of an up-front commitment by Cloud users, thereby allowing companies to start small and increase hardware resources only when there is an increase in their needs.
  3. The ability to pay for use of computing resources on a short-term basis as needed (e.g., processors by the hour and storage by the day) and release them as needed, thereby rewarding conservation by letting machines and storage go when they are no longer useful.
We argue that the construction and operation of extremely large-scale, commodity-computer datacenters at low-cost locations was the key necessary enabler of Cloud Computing, for they uncovered the factors of 5 to 7 decrease in cost of electricity, network bandwidth, operations, software, and hardware available at these very large economies of scale. These factors, combined with statistical multiplexing to increase utilization compared a private cloud, meant that cloud computing could offer services below the costs of a medium-sized datacenter and yet still make a good profit. Any application needs a model of computation, a model of storage, and a model of communication. The statistical multiplexing necessary to achieve elasticity and the illusion of infinite capacity requires each of these resources to be virtualized to hide the implementation of how they are multiplexed and shared. Our view is that different utility computing offerings will be distinguished based on the level of abstraction presented to the programmer and the level of management of the resources.

Amazon EC2 is at one end of the spectrum. An EC2 instance looks much like physical hardware, and users can control nearly the entire software stack, from the kernel upwards. This low level makes it inherently difficult for Amazon to offer automatic scalability and failover, because the semantics associated with replication and other state management issues are highly application-dependent. At the other extreme of the spectrum are application domain-specific platforms such as Google AppEngine. AppEngine is targeted exclusively at traditional web applications, enforcing an application structure of clean separation between a stateless computation tier and a stateful storage tier. AppEngine's impressive automatic scaling and high-availability mechanisms, and the proprietary MegaStore data storage available to AppEngine applications, all rely on these constraints. Applications for Microsoft's Azure are written using the .NET libraries, and compiled to the Common Language Runtime, a language-independent managed environment. Thus, Azure is intermediate between application frameworks like AppEngine and hardware virtual machines like EC2. When is Utility Computing preferable to running a Private Cloud? A first case is when demand for a service varies with time. Provisioning a data center for the peak load it must sustain a few days per month leads to underutilization at other times, for example. Instead, Cloud Computing lets an organization pay by the hour for computing resources, potentially leading to cost savings even if the hourly rate to rent a machine from a cloud provider is higher than the rate to own one. A second case is when demand is unknown in advance. For example, a web startup will need to support a spike in demand when it becomes popular, followed potentially by a reduction once some of the visitors turn away. Finally, organizations that perform batch analytics can use the "cost associativity" of cloud computing to finish computations faster: using 1000 EC2 machines for 1 hour costs the same as using 1 machine for 1000 hours. For the first case of a web business with varying demand over time and revenue proportional to user hours, we have captured the tradeoff in the equation below.

The left-hand side multiplies the net revenue per user-hour by the number of user-hours, giving the expected profit from using Cloud Computing. The right-hand side performs the same calculation for a fixed-capacity datacenter by factoring in the average utilization, including nonpeak workloads, of the datacenter. Whichever side is greater represents the opportunity for higher profit.

The table below previews our ranked list of critical obstacles to growth of Cloud Computing; the full discussion is in Section 7 of our paper. The first three concern adoption, the next five affect growth, and the last two are policy and business obstacles. Each obstacle is paired with an opportunity, ranging from product development to research projects, which can overcome that obstacle.

We predict Cloud Computing will grow, so developers should take it into account. All levels should aim at horizontal scalability of virtual machines over the efficiency on a single VM. In addition:

  • Applications Software needs to both scale down rapidly as well as scale up, which is a new requirement. Such software also needs a pay-for-use licensing model to match needs of Cloud Computing.
  • Infrastructure Software needs to be aware that it is no longer running on bare metal but on VMs. Moreover, it needs to have billing built in from the beginning.
  • Hardware Systems should be designed at the scale of a container (at least a dozen racks), which will be is the minimum purchase size. Cost of operation will match performance and cost of purchase in importance, rewarding energy proportionality such as by putting 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.
Table: Quick Preview of Top 10 Obstacles to and Opportunities for Growth of Cloud Computing.
ObstacleOpportunity
1Availability of ServiceUse Multiple Cloud Providers; Use Elasticity to Prevent DDOS
2Data Lock-InStandardize APIs; Compatible SW to enable Surge Computing
3Data Confidentiality and AuditabilityDeploy Encryption, VLANs, Firewalls; Geographical Data Storage
4Data Transfer BottlenecksFedExing Disks; Data Backup/Archival; Higher BW Switches
5Performance UnpredictabilityImproved VM Support; Flash Memory; Gang Schedule VMs
6Scalable StorageInvent Scalable Store
7Bugs in Large Distributed SystemsInvent Debugger that relies on Distributed VMs
8Scaling QuicklyInvent Auto-Scaler that relies on ML; Snapshots for Conservation
9Reputation Fate SharingOffer reputation-guarding services like those for email
10Software LicensingPay-for-use licenses; Bulk use sales

282 comments:

  1. Nice paper; no surprises, but thorough. The only thing I have to add that while AppEngine does have an "impressive ... high-availability mechanism," services like EC2 may eventually be able to take advantage of continuous live-migration (e.g. Kemari, Remus) to increase availability of particular virtual machines. The technique isn't free, and would likely be applicable only to certain classes of application, but it has potential by virtue of being generic (i.e. in theory requiring no application-level changes).

    (I posted this comment over at James Hamilton's blog, too, but got an error submitting it, so it may not appear there.)

    ReplyDelete
  2. Hi,
    Interested paper. However, may i ask to have some more details about the obstacle number 5, specially the part related to the gang scheduling? in this part do you mean to add this kind of support to the hypervisor layer? or to the operating system?
    more details is much appreciated, as i am interested in parallel programming and virtualization at the same time, and i might think about this further.

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  3. This is an excellent paper. It's a good idea to provide an executive summary and a video so that individuals can absorb the material at the level that suits them.

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  4. Another impediment - to companies at least - is goung to be confidentiality and security. I will be a hard sell in my company to allow any company-confidential data out of the door.

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  5. Comment from James A. Colgan:

    A very interesting and informative paper. Thank you for your research. In particular the performance and bandwidth calculations and trade-offs are most informative.

    There are a few more drivers towards a cloud computing model I would suggest:

    - Piracy: A SaaS model provides a much richer defense against piracy when compared to shipping CDROM's or downloads.

    - Understanding User Behavior: Great is the contention between product management and development about how a product or feature is actually being used. Or how valuable and useful a potential feature could be. Within a SaaS model the vendor now has complete knowledge and therefore knows where to focus development resources.

    - Further cost savings: DataCenter IT human resources are expensive with a great deal of duplication across an industry. Moving this expense from the user to the SaaS vendor to be amortized has tremendous value.

    I touch on these points and bring out some others that are specific to a particular industry in my serial blog post:

    "Cloud Computing, SaaS, and Electronic Design"
    http://tinyurl.com/5w7f2z

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  6. Nice paper in general. I have a question though. So the paper defines cloud computing = SaaS + PaaS in which SaaS providers = PaaS users.

    It gave an example of some scientists who want to batch process their data. But I don't think that those scientists are SaaS providers. I wonder if the given definition is general enough?

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  7. Hi. That's a great and thorough paper. I enjoyed reading it a lot.

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  8. This paper will be quoted relentlessly in the future as it provides a solid foundation on which others will lay their bricks.

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  9. I'm sure that I'm being very dumb, but what does ML stand for (Obstacle 8)?

    ReplyDelete
  10. Definitely well-written; nice to see some Cloud commentary by folks that know how to write!

    It's nice to see folks with an obvious talent for writing attempt to reset the Cloud hype!

    Since the Cloud is so much markitechture, I can't resist giving my definition:

    Computing == Distributed computing attempting to achieve mainframe class of service.

    Not that I’m that I’m a big fan of big iron...aka a big hole in the ground you pour money into.... The problems with big iron: BIG capital and operating expense, bureuacracy, overhead for small needs, resistance to change.

    To paraphrase others, the best way to big is to start small. If you can get your distributed architecture to work in the small, while anticipating scale, you’ll do well on the Cloud. This doesn’t come for free. One of Amazon’s poster childs is Animoto. What they don’t often say is they had to stop feature development for 3 months while they refactored (a fancy way of saying rewrote) their app.

    Amazon and other public clouds can justifiably claim the illusion of infinite scale. There are lots of other -ilities, however, that it’s difficult for black boxes to achieve. Their abstractions leak and usually require remodeling of your current approach to scaling, security, availability, etc.

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