  It is estimated that data centers constitute 1.5% of global electricity
usage. At the same time, to serve increasing user requirements, modern cloud
providers are operating multiple geographically distributed data centers.
Distributed data center infrastructure changes the rules of cloud control, as
energy costs depend on current regional electricity prices and temperatures
that we call geotemporal inputs. Furthermore, pricing policies at which cloud
providers can offer computational resources depend on the quality of service
(QoS). With such pricing schemes and the increasing energy costs in data
centres, balancing energy savings with performance and revenue losses is a
challenging problem. Existing cloud control methods are suitable only for a
single data center or do not consider all the available cloud control actions
that can reduce energy costs in geographically distributed data centers. In
this thesis, we propose a pervasive cloud control approach consisting of
multiple methods for dynamic resource reallocation and hardware configuration
adapted to volatile geotemporal inputs. The proposed methods consider the QoS
impact of cloud control actions and the data quality limits of time series
forecasting methods. We offer a cloud controller design that supports future
extensions when new decision support components need to be added. We also
propose novel pricing schemes which account for the computational resource
availability and costs that arise from our cloud control approach to enable
both flexible, energy-aware and high performance cloud computing. We evaluate
our methods empirically and in a number of simulations using historical traces
of electricity prices, temperatures, workloads and other data. Our results show
that significant energy cost savings are possible without harming the QoS or
service revenue in geographically distributed cloud computing.
