# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 1 Introduction
The immediate usage of green energy is the most convenient option, but can be exploited only if the infrastructure is able to support the workload redistribution and if efficient algorithms are designed to drive these workload shifts. In the paper, we prove that it is possible to effectively use green energy to reduce operational cost with a smart workload distribution. In this paper, we present a novel approach for the efficient exploitation of RES in a distributed data center scenario, by adapting and refining a workload management strategy already proposed in the literature, i.e., EcoMultiCloud [2]. EcoMultiCloud includes a hierarchical architecture for the management of geographically-distributed data centers and a set of algorithms that drive the assignment and migrations of virtual machines (VMs) on the basis of the technical and business objectives defined by the management. EcoMultiCloud is composed of two layers: at the lower layer, each site adopts its own strategy to distribute and consolidate the workload internally. At the upper layer, a set of algorithms--shared by all the sites--are used to evaluate the behavior of single sites and distribute the workload among them. This architecture offers several benefits, among which: (i) scalability, because the bigger problem of workload allocation is decomposed into smaller intra-data center an inter-data center problems; (ii) autonomy of single data centers, since each data center can adopt its own algorithm for internal allocation; (iii) flexibility, since the algorithms can be easily adapted in accordance with the desired objectives. In [2], the objectives that drove the workload redistribution were the load balancing among the sites and the minimization of costs; in that paper, the flexibility and feasibility of EcoMultiCloud were also verified with the support of analytical models. However, the availability of RES was not considered. In this paper, thanks to the flexibility characteristics mentioned above, we adapt the EcoMultiCloud algorithm to exploit the presence of renewable energy plants specifically. The EcoMultiCloud algorithm proves to be suited to the new, more challenging scenario, in which RES is introduced. EcoMultiCloud accomplishes the objective to use renewable energy when and where it is generated; energy produced in excess with respect to what is consumed can be stored for future use. We analyzed a scenario with four data centers, located in various geographical areas and with different patterns of renewable energy generation. Performance results mainly concern the reduction in the use of grid energy and the cost savings that derive from this reduction. Results were derived when varying the size of photovoltaic panels and the size of batteries, which allowed us to determine the appropriate values of both parameters. Moreover, we evaluated the advantages deriving from the use of the novel strategy with respect to a strategy that does not exploit migrations and to a strategy that moves the workload randomly from the most costly data center to the other data centers. The main contribution of the paper is two-fold. On the one hand, the paper shows that, while it is intermittent and highly variable, renewable energy can be effectively used in geographically-distributed data centers to reduce operational costs due to the infrastructure power supply, given that a smart and dynamic workload management is adopted. On the other hand, the paper proves that EcoMultiCloud, used in the past only in scenarios with traditional power supply, is suited for the purpose and can be easily adapted to consider green energy in the scenario. The paper is organized as follows.