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Infrastructure labor costs. While cloud computing significantly lowers labor costs at any scale by
automating many repetitive management tasks, larger facilities are able to lower them further than
smaller ones. While a single system administrator can service approximately 140 servers in a traditional
enterprise,
in a cloud data center the same administrator can service thousands of servers. This allows
IT employees to focus on higher value-add activities like building new capabilities and working through
the long queue of user requests every IT department contends with.
Security and reliability. While often cited as a potential hurdle to public cloud adoption, increased
need for security and reliability leads to economies of scale due to the largely fixed level of investment
required to achieve operational security and reliability. Large commercial cloud providers are often
better able to bring deep expertise to bear on this problem than a typical corporate IT department,
thus actually making cloud systems more secure and reliable.
Buying power. Operators of large data centers can get discounts on hardware purchases of up to
30 percent over smaller buyers. This is enabled by standardizing on a limited number of hardware
and software architectures. Recall that for the majority of the mainframe era, more than 10 different
architectures coexisted. Even client/server included nearly a dozen UNIX variants and the Windows
Server OS, and x86 and a handful of RISC architectures. Large-scale buying power was difficult in
this heterogeneous environment. With cloud, infrastructure homogeneity enables scale economies.
Going forward, there will likely be
many additional economies of
scale that we cannot yet foresee.
The industry is at the early
stages of building data centers at
a scale we’ve never seen before
(Fig. 5). The massive aggregate
scale of these mega DCs will
bring considerable and ongoing
R&D to bear on running them
more efficiently, and make them
more efficient for their customers.
Providers of large-scale DCs, for
which running them is a primary
business goal, are likely to
benefit more from this than
smaller DCs which are run inside
enterprises.
2.2 Demand-Side Economies of Scale
The overall cost of IT is determined not just by the cost of capacity, but also by the degree to which the
capacity is efficiently utilized. We need to assess the impact that demand aggregation will have on costs
of actually utilized resources (CPU, network, and storage).
In the non-virtualized data center, each application/workload typically runs on its own physical server.
This means the number of servers scales linearly with the number of server workloads. In this model,
Source: James Hamilton, Microsoft Research, 2006.
In this paper, we talk generally about “resource” utilization. We acknowledge there are important differences among resources.
For example, because storage has fewer usage spikes compared with CPU and I/O resources, the impact of some of what we
discuss here will affect storage to a smaller degree.
FIG. 5: RECENT LARGE DATA-CENTER PROJECTS
Sources: Press releases.