# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 4 Workload Assignment and Migration  
As mentioned in the previous section, a key responsibility of the DCM is to analyze detailed data about the local data center and summarize relevant information that is then transmitted to remote DCMs and used for the assignment and redistribution of the workload. EcoMultiCloud is very flexible and can take into account several pieces of high level information. This information changes based on the general objectives of the multi-site infrastructure. Possible objectives are: the reduction of consumed energy, the reduction of carbon emissions, the quality of service in terms of the amount of  
Figure 2: The considered scenario: four DCs equipped with PV panels and energy storage units use EcoMultiCloud for coordinating workload management among the DCs. resources devoted to each VM, the load balance among the sites, the reduction of data transmitted among DCs, and so on. All the above-mentioned goals are important, yet different data centers may focus on different aspects, depending on the specific operating conditions and on the priorities prescribed by the management. It is up to the company's management to specify the objectives and their relative weights. In this paper, the focus being the benefits of RE generation in multi-site DC operation and the associated reduction of operational costs, the algorithm uses information such as the amount of produced RE, the price of electricity taken from the power grid and the efficiency of the DCs. In EcoMultiCloud, a key role in the distribution of the workload among the DCs is played by the assignment function. The assignment function is sort of a general key performance indicator that is associated in any moment with each DC. The assignment function balances and weights the chosen business goals, combining the DC information that was mentioned above. The numerical value that the assignment function associates with a DC represents the cost to run some workload in that DC: low values correspond to low overall cost of the DC. The strategy, then, is to assign a VM to the DC with the lowest value of the function. In its general formulation, the assignment function \(f^{i}_{assign}\), for each DC \(i\), is defined as follows:  
\[f^{i}_{assign}=\sum_{i=1}^{M}\alpha_{i}\frac{F_{i}}{F_{max}}\qquad\qquad\sum_ {i=1}^{M}\alpha_{i}=1 \tag{1}\]  
where \(M\) is the number of performance indicators and the coefficients \(\alpha_{i}\) are the weights that decide the balance among the various targets, and they are decided by the system administrator based on the strategic decisions on how the system should work. The terms \(F_{i}\) are the performance indicators that represent the various targets, for example carbon emissions, overall utilization and energy cost. In order to combine indicators that have different units of measure and are not directly comparable, each indicator is normalized with respect to the maximum values communicated by DCs. After computing the value of \(f_{assign}\) for each DC, the VM is assigned to the data center having the lowest value. Once designed to the target DC, the VM is locally managed and allocated to a physical host using the local assignment algorithm. Clearly, the choice above can also be combined with other rules that include QoS constraints (e.g., it can be requested that the distance/delay between the DC and the user is smaller than a given target) or decisions based on the kind of VM (e.g., interactive versus non-interactive VMs). _Abbreviations_ reports the notation used throughout the paper. In this paper, the main objective is to evaluate the impact of the introduction of RE generation into the operation of geo-distributed DCs. Hence, the assignment function is modified and adapted in the following way:  
\[f^{i}_{assign}=\left(C_{i}-E_{i}\right)P_{i} \tag{2}\]  
where \(C_{i}\) is the consumed energy estimated for the additional workload, \(E_{i}\) is the green produced energy that is still available and \(P_{i}\) is the price of energy from the power grid.