  An electricity bill of a data center (DC) is determined not only by how much
energy the DC consumes, but especially by how the consumed energy is spread
over time during the billing cycle. More specifically, these electricity costs
are essentially made up of two major charges: Energy Charge, a cost based on
the amount of consumed energy (in kWh), and Peak Charge, a cost based on the
maximum power (in kW) requested during the billing cycle. The latter charge
component is forced to encourage DCs to balance and regulate their power
demands over the billing cycle, allowing the utility company to manage
congestion without increasing supply. This billing model has thus called for
the development of peak power shaving approaches that reduce costs by smoothing
peak power demands over the billing cycle to minimize the Peak Charge
component. In this paper, we investigate peak power shaving approaches, and
begin by using Google data traces to quantify and provide a real sense of how
much electricity cost reduction can peak power demand shaving achieve on a
Google DC cluster. We then discuss why peak power shaving is well-suited for
reducing electricity costs of DCs, and describe two commonly used peak shaving
approaches, namely energy storage and workload modulation. We finally identify
and describe key research problems that remain unsolved and require further
investigation.
