# Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study
## 4 SDN-Based Cloud Computing Resource Management in Energy Efficiency
It works by receiving traffic status by the optimizer, which is implemented with various methods depending on providers' goals: power saving, performance and fault tolerant. After that, a subnet of topology and flow path are sent to control the software. The system is evaluated in the testbed and proved its capability of supporting performance and robustness while reducing the power consumption. In addition to that, by analyzing traffic bandwidth usage in datacenter networks, CARPO [26] is proposed to reduce the power consumption through utilizing the minimumsubnet of the network. By tracing Wikipedia and Yahoo traces, various traffic peak time and correlation are observed. This leads to conclude that correlation is either to be loosely or negative and consolidation of traffic value is suggested to be designed, depending on the on and off peak values. Therefore, CARPO works by analyzing correlation from previous period, then consolidates traffic based on link capacity and, finally, adapts the link rates. It is evaluated based on power saving, power consumption, packet drop ratio and delay using testbed and simulation. Results show that delay and packet drop ratio are considered higher than baselines; however, CARPO succeeds in power saving. Moreover, DCTCP is proposed TCP-like protocol that is specified for datacenter networks [27]. It works by estimating cognition degree from fraction of marked packets extracted from Explicit Congestion Notification (ECN). Thus, DCTCP enrich ECN with control schema in order to reduce buffer space while maintain network performance. While a study showed that a scalable network topology; flattened butterfly with commercial switch chips provides full utilization with reduced energy proportional datacenter [28]. Whereas, in [29], VM placement is addressed by combining two approaches that are genetic and the tabue search algorithms to reduce power consumption in cloud environment. Tabue search algorithm is used to enhance genetic algorithm searching by performing mutation operations. GATA algorithm is evaluated using PyCharm with Google Cluster dataset. Results show that GATA manages to reduce power consumption while lowering load balance. Additionally, it gives highest number of optimal solutions for different size problems. However, execution time is affected as it falls in the third-best position compared to four baselines. As well, HGAPSO is a multi-objective resource allocation based on Euclidean distance, which combines two algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for VM placement. It comprises three main phases: VM allocation, task scheduling, and VM migration. It focused on reducing the power consumption while increasing the resource utilization. The results of evaluation show that HGAPSO consumes more energy and less resources (CPU, RAM and storage). However, it works much better when combined with VM migration in term of both energy consumption and resource utilization [30]. Moreover, multi-objective virtual cluster allocation framework is proposed in [31] based on four tradeoff models, which are VM availability, saving energy, resource utilization and load balance. Then IBBBO algorithm is developed based on natural evolutionary algorithm in which weights are assigned for previous models as a tradeoff relationship. It is tested using CloudSim where power consumption, average resources utilization, risk value and load balance value are measured. Results show that IBBBO manages in obtaining lower risk value, less power consumption, better average resources utilization with the least load balance value compared to most of the baselines. In addition to that, several algorithms are proposed to find the acceptable balance between energy consumption and SLA violation, such as in [32], a framework for resource management that considers VM placement in such a way that SLA is maintained, and the energy consumption is reduced.