# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 2 Related Work  
Many successful efforts have been made to increase the physical efficiency of data centers; for example, for its components devoted to cooling and power distribution, and this is confirmed by the general decrease of the PUE (power usage effectiveness index), the ratio between the overall power entering the data center and the power needed for the IT infrastructure. However, much remains to be done in terms of the computational efficiency: for example, on average, only a fraction of CPU capacity of servers--between 15% and 30%--is actually exploited, and this leads to huge inefficiencies due to the lack of proportionality between resource usage and energy consumption [3]. Improvements in this field are related to a more efficient management of the workload and a better use of the opportunities offered by virtualization. The efforts may be categorized into two big fields: workload consolidation within a single data center and efficient workload management in geographical infrastructures that include several remote data centers. Workload consolidation is a powerful means to improve IT efficiency and reduce power consumption within a data center [4; 5; 6; 7]. In [8], the authors presented a multi-resource scheduling technique to provide a higher degree of consolidation in multi-dimensional computing systems. In [9], a novel model was proposed for allocating virtual elements in a software-defined cloud data center, jointly minimizing the energy consumption deriving from computing, data transmission and VM migrations. The authors in [10] proposed an algorithm to manage the power states of the servers in a cloud data center, in order to minimize the electricity consumption and maintenance costs derived from the power variation on the servers' CPU. The algorithm was deployed taking into account the costs for VMs processing on the servers, for transferring data between the VMs and for migrating the VMs across the servers. Some approaches--e.g., [11; 12]-try to forecast the processing load and aim at determining the minimum number of servers that should be switched on to satisfy the demand, so as to reduce energy consumption and maximize data center revenues. However, even a correct setting of this number is only a part of the problem: algorithms are needed to decide how the VMs should be mapped to servers in a dynamic environment and how live migration of VMs can be exploited to unload servers and switch them off when possible, or to avoid SLA violations. Self-organizing and decentralized algorithms are proposed to improve scalability, since the problem of consolidation is known to be NP-hard. In [13], the data center was modeled as a P2P network, and ant-like agents explored the network and collected information needed to migrate VMs and reduce power consumption. The approach presented in [14] decentralized part of the intelligence to single servers that made decisions based on local information, using probabilistic functions, while a central manager coordinated servers' decisions to consolidate the workload efficiently. The problem is even more complex in geographically-distributed data centers. Research efforts are focused on two related, but different aspects [15]: the routing of service requests to the most efficient data center, in the so-called assignment phase, and the live migration of portions of the workload when conditions change and some data centers become preferable in terms of electricity costs, emission factors or more renewable power generation. Several studies explored the opportunity of energy cost-saving by routing jobs when/where the electricity prices were lower [16; 17]. Some prior studies assumed that the electricity price variations and/or job arrivals follow certain stationary (although possibly unknown) distributions [18; 19; 20]. [PERSON] et al. [21] tackled the problem taking into account the spatial and time diversity in dynamic electricity markets. They attempted to minimize overall costs for multiple data centers located in different energy market regions. [PERSON] et al.