# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 2 Related Work
In order to adapt to the dynamic availability of renewable energy, the authors in [31] argued for either pausing VM executions or migrating VMs between sites based on local and remote energy availability. Some recent works that coped with the real-time energy-efficient distribution of load in a geographical scenario were included in a special issue devoted to green and energy-efficient cloud computing [32]. The authors of [33] focused on the energy-efficient execution of scientific workflows that needed to be deployed across multiple data centers due to their large-scale characteristics. The optimal allocation of virtual machines must consider that workflow tasks have dependencies and communication constraints, which make them differ significantly from unrelated tasks. An energy consumption model was devised, and a corresponding energy-aware resource allocation algorithm was proposed for virtual machine scheduling. The energy-efficient management of geographically-distributed data centers was also the subject of [34]. The authors focused on the significant impact of \"geotemporal input\", i.e., the time- and location-dependent factors that may impact energy consumption. Among such factors, they considered real-time electricity pricing enabled by the deregulated electricity market, the cooling efforts needed at different sites and different times and the availability of renewable energy. The scheduling of the VMs was tackled through a two-stage approach, which combined best-effort global optimization, driven by genetic algorithms, with deterministic local optimization for constraint satisfaction. [PERSON] et al. [35] used the idea of migrating VMs in a federated cloud environment to reduce costs and carbon footprint. They took advantage of dynamic electricity pricing to migrate the VMs to the data center with the lowest energy cost and move the virtual machines from a high carbon footprint source to data centers with the best access to renewable energy. Most proposed approaches aimed to solve the problem as a whole, in a centralized fashion, undergoing the risk of originating three main issues, as discussed in the introductory section: poor scalability due to the size of the problem and the heterogeneity of involved business objectives, poor ability to adapt to changing conditions (e.g., changes in amount of workload, electricity price or carbon taxes) and lack of autonomy of single data centers. To cope with these issues efficiently, we believe that it is necessary to decentralize part of the intelligence and distribute the decisions points, while still exploiting the centralized architecture and functionalities offered by virtualization infrastructures in single data centers. This naturally leads to a hierarchical infrastructure, in which single data centers manage the local workload autonomously, but communicate with each other to route and migrate VMs among them. A self-organizing hierarchical architecture was proposed in [36], but so far, it has been limited to the management of a single data center. A recent study [37] proposed a hierarchical approach that combined inter-DC and intra-DC request routing. The VM scheduling problem was decomposed and solved at single data centers and was able to combine different objectives, e.g., minimize electricity cost, carbon taxes and bandwidth cost. While the work certainly deserves attention, it only solved the routing problem and did not exploit the opportunity of dynamic workload migration, nor does the approach seem to be easily extensible in that direction. In [2], the authors presented EcoMultiCloud, a two-level architecture that is able to assign and redistribute the workload on the sites of a geographically-distributed infrastructure. The EcoMultiCloud algorithms for workload distribution were designed to achieve a better load balancing and a reduction of costs, but the same algorithms can be customized for a variety of technical and business goals.