Gartner published the five attributes of cloud computing in 2009 and it remained a constant reminder of what Cloud truly is as we technologists start to introduce Cloud into HPC and analytics.
Service-Based: Consumer concerns are abstracted
from provider concerns through service interfaces that are well-defined.
The interfaces hide the implementation details and enable a completely
automated response by the provider of the service to the consumer of the
service. The service could be considered "ready to use" or "off the
shelf" because the service is designed to serve the specific needs of a
set of consumers, and the technologies are tailored to that need rather
than the service being tailored to how the technology works.
{Frank: this translates into having a service catalog that users can easily search, browse and request for cloud services such as analytics or HPC).
Scalable and Elastic: The service can scale
capacity up or down as the consumer demands at the speed of full
automation (which may be seconds for some services and hours for
others). Elasticity is a trait of shared pools of resources. Scalability
is a feature of the underlying infrastructure and software platforms.
Elasticity is associated with not only scale but also an economic model
that enables scaling in both directions in an automated fashion. This
means that services scale on demand to add or remove resources as
needed.
{Frank: this remains the biggest challenge for HPC Cloud as resource are not yet optimized based on virtualization and high-performance network such as Infiniband}
Shared: Services share a pool of resources to
build economies of scale. IT resources are used with maximum efficiency.
The underlying infrastructure, software or platforms are shared among
the consumers of the service (usually unknown to the consumers). This
enables unused resources to serve multiple needs for multiple consumers,
all working at the same time.
{Frank: resource sharing hasn't been an issue as HPC infrastructure has traditionally been designed as a centralized and shared resource. Case in point: Grid computing}
Metered by Use: Services are tracked with usage
metrics to enable multiple payment models. The service provider has a
usage accounting model for measuring the use of the services, which
could then be used to create different pricing plans and models. These
may include pay-as-you go plans, subscriptions, fixed plans and even
free plans.
{Frank: now you are not owning but leasing - so welcome to the new world of pay-as-you-go}
Uses Internet Technologies: The service is
delivered using Internet identifiers, formats and protocols, such as
URLs, HTTP, IP and representational state transfer Web-oriented
architecture.
{Frank: this is obvious. I wouldn't call this out as an attribute but will take it nevertheless}
Link:
Update:
- 2012.06.18: original post
Does the architecture of private clouds allow them to be easily expanded? Would it be relatively easy to scale up, but not so easy to scale back? Economically, this might be described as ‘pay as you go’ rather than ‘pay only for what you use.’ Users in a campus setting would still benefit by sharing the cost of a shared resource. Do I understand that correctly?
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