# Reducing the Operational Cost of Cloud Data Centers through Renewable Energy
## 2 Related Work
in [22] studied the effect of transmission delay introduced by the routing of service requests and related data across DCs. The authors in [19] proposed a solution in which the power cost could be reduced under delay-tolerant workloads. By exploiting temporal and spatial variations of both workload and electricity prices, they provided a power cost-delay trade-off, which was exploited to minimize power expenses at the cost of service delay. The considered target applications that can generate delay-tolerant workloads are based on MapReduce programming, including searching, social networking and data analytics. [PERSON] et al. in [16] proposed a geographical load balancing (GLB) approach to route general Internet service-requests to data centers located in various geographical regions, by computing the optimal number of active servers at each data center. In [23], [PERSON] et al. proposed a GLB algorithm to minimize energy cost and control the risks at the same time, as they modeled the uncertainties of price and workload as risk constraints. In [24], [PERSON] et al. exploited the temporal and spatial diversities of energy price to trade service delay for energy cost. The authors proposed a novel spatio-temporal load balancing approach to minimize energy cost for distributed DCs. The algorithms presented in [25; 26] tackled the problem considering the user's point of view and aimed to choose the most convenient data center to which the user should consign a service or VM. The authors of [1] investigated different parameters that affect energy and carbon cost for a cloud provider with geo-distributed data center sites. The overhead energy of a data center, which is responsible for a portion of the energy consumption as large as half its value in old sites, is considered by defining a model for PUE as a function of data centers' IT load and outside temperature. The devised algorithms aimed to improve access to renewable energy sources, since data center sites that can get their power from renewable sources help the provider decrease its dependency on the electricity drawn from off-site grids, which are costly and less clean. Inter-DC VM migration is a more novel research topic, as virtualization infrastructures have offered such features only recently: for example, the vSphere 6.0 release of VMware includes new long-distance live migration capabilities, which will enable VM migrations across remote virtual switches and data centers. While opportunities opened by long-distance migrations are big, the issues involved are also extremely complex: among them, determining whether the benefits of workload migrations overcome the drawbacks, from which site and to which site to migrate, what specific portion of the workload should be migrated, how to reassign the migrating workload in the target site, etc. Some significant efforts have been done in this area. The electricity price variation, both across time and location, was exploited to reduce overall costs using different strategies. The Stratus approach [27] exploited Voronoi partitions to determine to which data center requests should be routed or migrated. [PERSON] et al. [28] used an online scheduling algorithm based on Lyapunov optimization techniques. In [29],[PERSON] et al. proposed an optimization framework based on the observation that energy prices and query workloads showed high spatio-temporal variation for throughput-intensive applications like web search engines. The optimization framework was based on a workload shifting algorithm considering both electricity prices, to reduce the energy cost, and workload of data centers at the time of shifting, to reduce response time. [PERSON] et al. considered VM placement in the cloud for high performance applications [30]. The authors proposed VM migration policies across multiple data centers in reaction to variable power pricing.