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As computer systems evolve, the volume of data to be processed
increases significantly, either as a consequence of the expanding
amount of available information, or due to the possibility of
performing highly complex operations that were not feasible in the
past. Nevertheless, tasks that depend on the manipulation of large
amounts of information are still performed at large computational
cost, i.e., either the processing time will be large, or they will
require intensive use of computer resources. In this scenario, the
efficient use of available computational resources is paramount,
and creates a demand for systems that can optimize the use of
resources in relation to the amount of data to be processed. This
problem becomes increasingly critical when the volume of
information to be processed is variable, i.e., there is a seasonal
variation of demand. Such demand variations are caused by a variety
of factors, such as an unanticipated burst of client requests, a
time-critical simulation, or high volumes of simultaneous video
uploads, e.g. as a consequence of a public contest. In these cases,
there are moments when the demand is very low (resources are almost
idle) while, conversely, at other moments, the processing demand
exceeds the resources capacity. Moreover, from an economical
perspective, seasonal demands do not justify a massive investment
in infrastructure, just to provide enough computing power for peak
situations. In this light, the ability to build adaptive systems,
capable of using on demand resources provided by Cloud Computing
infrastructures is very attractive.
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