Presentation by Jose M. Moya at the IEEE Region 8 SB & GOLD Congress (25 – 29 July, 2012).
The current techniques for data center energy optimization, based on
efficiency metrics like PUE, pPUE, ERE, DCcE, etc., do not take into
account the static and dynamic characteristics of the applications and
resources (computing and cooling). However, the knowledge about the
current state of the data center, the past history, the resource
characteristics, and the characteristics of the jobs to be executed
can be used very effectively to guide decision-making at all levels in
the datacenter in order to minimize energy needs. For example, the
allocation of jobs on the available machines, if done taking into
account the most appropriate architecture for each job from the
energetic point of view, and taking into account the type of jobs that
will come later, can reduce energy needs by 30%.
Moreover, to achieve significant reductions in energy consumption of
state-of-the-art data centers (low PUE) is becoming increasingly
important a comprehensive and multi-level approach, ie, acting on
different abstraction levels (scheduling and resource allocation,
application, operating system, compilers and virtual machines,
architecture, and technology), and at different scopes (chip, server,
rack, room, and multi-room).