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2.1 Supply-Side Economies of Scale
Cloud computing combines the best
economic properties of mainframe and
client/server computing. The mainframe
era was characterized by significant
economies of scale due to high up-front
costs of mainframes and the need to hire
sophisticated personnel to manage the
systems. As required computing power –
measured in MIPS (million instructions per
second) – increased, cost declined rapidly
at first (Fig. 4), but only large central IT
organizations had the resources and the
aggregate demand to justify the
investment. Due to the high cost, resource
utilization was prioritized over end-user
agility. Users’ requests were put in a
queue and processed only when needed
resources were available.
With the advent of minicomputers and later client/server technology, the minimum unit of purchase
was greatly reduced, and the resources became easier to operate and maintain. This modularization
significantly lowered the entry barriers to providing IT services, radically improving end-user agility.
However, there was a significant utilization tradeoff, resulting in the current state of affairs: datacenters
sprawling with servers purchased for whatever need existed at the time, but running at just 5%-10%
utilization
.
Cloud computing is not a return to the mainframe era as is sometimes suggested, but in fact offers users
economies of scale and efficiency that exceed those of a mainframe, coupled with modularity and agility
beyond what client/server technology offered, thus eliminating the tradeoff.
The economies of scale emanate from the following areas:
Cost of power. Electricity cost is rapidly rising to become the largest element of total cost of ownership
(TCO),
currently representing 15%-20%. Power Usage Effectiveness (PUE)
tends to be significantly
lower in large facilities than in smaller ones. While the operators of small data centers must pay the
prevailing local rate for electricity, large providers can pay less than one-fourth of the national average
rate by locating its data centers in locations with inexpensive electricity supply and through bulk
purchase agreements.
In addition, research has shown that operators of multiple data centers are able
to take advantage of geographical variability in electricity rates, which can further reduce energy cost.
Source: The Economics of Virtualization: Moving Toward an Application-Based Cost Model, IDC, November 2009.
Not including app labor. Studies suggest that for low-efficiency datacenters, three-year spending on power and cooling,
including infrastructure, already outstrips three-year server hardware spending.
Power Utilization Effectiveness equals total power delivered into a datacenter divided by “critical power” – the power
needed to actually run the servers. Thus, it measures the efficiency of the datacenter in turning electricity into computation.
The best theoretical value is 1.0, with higher numbers being worse.
Source: U.S. Energy Information Administration (July 2010) and Microsoft. While the average U.S. commercial rate
is 10.15 cents per kilowatt hour, some locations offer power for as little as 2.2 cents per kilowatt hour
FIG. 4: ECONOMIES OF SCALE (ILLUSTRATIVE)
Source: Microsoft.
