  Data warehouse performance is usually achieved through physical data
structures such as indexes or materialized views. In this context, cost models
can help select a relevant set ofsuch performance optimization structures.
Nevertheless, selection becomes more complex in the cloud. The criterion to
optimize is indeed at least two-dimensional, with monetary cost balancing
overall query response time. This paper introduces new cost models that fit
into the pay-as-you-go paradigm of cloud computing. Based on these cost models,
an optimization problem is defined to discover, among candidate views, those to
be materialized to minimize both the overall cost of using and maintaining the
database in a public cloud and the total response time ofa given query
workload. We experimentally show that maintaining materialized views is always
advantageous, both in terms of performance and cost.
