Abstract:
Datacenters have been the key system
infrastructure for cloud computing. The demand for
datacenter computing has increased significantly in
recent years resulting in huge energy consumption.
Renewable energy resources, such as wind and solar
power are rapidly becoming generation technologies
of significance in the United States and around the
world. The integration of renewable energy resources
is usually very challenging because of their
intermittency and inter-temporal variations. The high
energy footprint of datacenters leads to serious
environmental issues. Energy expenditure has become
a significant fraction of datacenter operating costs.
The proposed system is explicitly modeled the
intermittent generation of renewable energy (Wind
Power Model and Solar Power Model) with respect to
varying weather conditions in the geographical
location of each datacenter. Renewable energy
resources datacenter selection framework is proposed
to reduce the environmental impact and the system
takes into account the benefit from the location
diversity of different types of available renewable
energy resources and an efficient datacenter selection algorithm is proposed major concern of broader research
community participating both from academia and
industry in the recent years.
Large Internet companies (e.g. Google and
Microsoft) have significantly improved the energy
efficiency of their multi-megawatt datacenters.
However, the majority of the energy consumed by
datacenters is actually due to countless small and
medium-sized datacenters, which are much less
efficient. These facilities range from a few dozen
servers housed in a machine room to several hundreds
of servers housed in a large enterprise installation.
These cost, infrastructure, and environmental
concerns have prompted some datacenter operator to
generate their own solar/wind energy of draw power
directly from a nearby solar/wind farm. Green energy
sources promise to mitigate the issues surrounding
non-renewable generation, but their output is very
susceptible to environmental changes.
