Lowering Down the Cost for Green Cloud Data Centers by Using ESDs and Energy Trading

Abstract

Cloud data centers contribute greatly to global warming, because most of the energy is generated by burning fossil fuels. In view of this, many cloud data centers are trying to power their data centers using renewable energy. In this paper, we propose a green scheduling architecture for the geographically distributed cloud data centers with time-varying and location-varying electricity prices. To lower down the energy cost and carbon emissions, each data center has its own wind turbines and solar panels. The generated renewable energy can be used to power data centers directly or stored into ESDs for latter use, or sold back to the power grid. However, it is hard to make decisions on the usage of each type of energy considering the dynamic incoming requests of users, fluctuating electricity prices, and intermittent energy supply in each time slot. Our problem is formulated as a mixed integer linear programming (MILP) problem: Given the arrival of incoming requests, schedule the requests, servers, and the usage of different energy sources, such that the total energy cost can be minimized while satisfying QoS requirement within certain carbon emission level. Our simulation is based on the traces from real world. Experiments show that our method can significantly lower down the energy cost for green cloud data centers by using ESDs and energy trading.