# Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study
## 3 SDN-Based Cloud Resource Management in Network Performance Improvement
### Contributions on VM Migration Task in Resources Management  
Although most of above-mentioned algorithms focused on VM placement, others consider VM migration as part of resource management, which targets QoS such as in [18], authors developed a live VM migration, S-CORE in SDN environment, which is derived from S-CORE in clouds [19]. S-CORE takes into consideration colocation constraints in order to minimize overall communication cost by assigning links with the ratio of over-subscription, as well the cost of bandwidth as the weights. Then, using temporal bandwidth utilization multiplied with aggregate weightings, overall cost is obtained. The system architecture consists of server virtualization to manage VMs through an open-source API; Libvert. While the software switch is Open vSwitch at the hypervisor level and SDN controller with OpenFlow specification. Moreover, the controller modules include topology discovery, host discovery, links weights, flow statistics and REST API with certain endpoints for controllers to migrate VM, depending on the network state. The proposed work is evaluated using simulation: NS3 and testbed, where, link utilization, overall communication cost, VM-VM communication cost, throughput, scalability, and Number of migrations are the evaluation metrics. The results show that there is a significant decrease in congestion, and the overall throughput is enhanced. Furthermore, this algorithm is proposed in clouds datacenters through tokens, which hold information such as the VM ID, communication level value to determine communication cost. As well, each VM has the token will have an advantage of determining the next VM to pass the token based on specific policy such as round robin. While Remedy [20] is proposed to manage VM migration by managing steady states. It is composed of three components that are bandwidth monitor, which will send VMs list to VM and target selector that determines a host to migrate VMs and VM memory monitor. The system is evaluated in testbed using real datacenter traces that shows the effectiveness of the Remedy system in reducing the unsatisfied bandwidth. The following table (Table 1) summarizes above mentioned network performance contributions in terms of major feature of algorithms, main tasks, the environments where the algorithm targets, which evaluation tool is used for testing and the dataset, evaluated metrics and, finally, the major contribution of the algorithm based on its published results. In addition to that, Table 2, declares evaluation metrics mapped with corresponding research. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref. ** & **Major Feature** & **Main Tasks** & **Env. ** & **Dataset** & **Eval. Tool** & **Measured Metrics** & **Contribution** \\ \hline \multirow{4}{*}{[7]} & \multirow{4}{*}{\(\bullet\)} & Empirical estimation of effective & \(\bullet\) & VM ensemble placement based on effective & \(\bullet\) & 300 MB dataset & \(\bullet\) & QoS overall and average & \(\bullet\) & Placing new VMs while maintaining bandwidth limits \\  & & MAPLE controller & bandwidth sums are placed on smart & \(\bullet\) & Tennis requests & DCN & proportion of delayed packets. & \(\bullet\) & Pressure QoS targets under violation rate \\  & & and EB agents. & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\