Magellan Final Report - Office of Science - U.S. Department of Energy

Chapter 12
Cost Analysis
One of the most common arguments made for the adoption of cloud computing is the potential cost saving
compared to deploying and operating in-house infrastructure. There are several reasons in support of this
argument. Commercial clouds consolidate demand across a large customer base, resulting economies of scales
that small departmental clusters cannot achieve. This includes lower number of FTEs per core, stronger
purchasing power when negotiating with the vendor, and better power efficiency since large systems can
justify investing more in the design of the cooling infrastructure. It is worth noting that large DOE HPC
Centers also consolidate demand and achieve many of the same benefits. Low upfront costs and pay-as-yougo
models are also considered advantages of clouds. Clouds allow users to avoid both time-investments and
costs associated with building out facilities, procuring hardware and services, and deploying systems. Users
are able to get access to on-demand resources and only pay for the services they use. Ultimately, whether a
cloud offering is less costly than owning and operating in-house resources is very dependent on the details of
the workload, including characteristics such as scaling requirements, overall utilization, and time criticality
of the workload. In this chapter, we will review some of the cost model approaches, consider the costs for
typical DOE centers, and discuss other aspects that can impact the cost analysis.
12.1 Related Studies
Walker [81] proposed a modeling tool that allows organizations to compare the cost of leasing CPU time from
a cloud provider with a server purchase. The paper provides an analysis comparing three options for NSF’s
Ranger supercomputing resources, i.e., purchase, lease, or purchase-upgrade, and shows that a three-year
purchase investment is the best strategy for this system. The analysis also considers a one-rack server and
shows that leasing is the better option for the system. Carlyle et. al. [9] provide a case study of costs
incurred by end-users of Purdue’s HPC community cluster program and conclude that users of the cluster
program would incur higher costs if they were purchasing commercial cloud computing HPC offerings such
as the Amazon Cluster compute program. Hamilton published an analysis of data center costs in 2008 [38]
and an updated analysis in 2010 [39]. His primary conclusion was that, despite wide-spread belief that power
costs dominated data center costs, server costs remained the primary cost factor.
12.2 Cost Analysis Models
There are various approaches to conducting cost comparison as discussed above. We present three approaches,
computing the hourly cost of an HPC system, the cost of a DOE center in a commercial cloud, and
a cost analysis using the HPC Linpack benchmark as a stand-in for DOE applications. These three models
are useful since they tackle the question from various dimensions—system, center, and user perspectives.
This first approach translates the operational cost an HPC system into the typical pricing unit used in
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