# GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters
## 4 GANA-VDC System Architecture
### Mapping Procedure
While the VDC allocations progressed, the network resources were affected negatively when they tried to determine which PAM to use for a VDC allocation based on the info regarding the global index. Usually, the time required for a VDC allocation ranged 100-600 ms. However, for intervals from 80% to 100%, sometimes data points rose to as high as 2 s. This was rationalized as some applications containing three-layer networks required a more thorough search of

Figure 10: Utilization of Network. (**a**) collect statistics of network utilization; (**b**) our proposed algorithm for VDC allocation. the index information. Indeed, in the case of a deficient network, at times, the whole index information might need to be traversed to detect a proper PAM. We can finally conclude, based on this analysis, the algorithm efficiently and adequately allocates the VDC. **Comparison:** As server network utilization reached 80% of its capacity, we tested eight VDCs allocations with 800 VMs to examine VDC algorithm efficiency. In comparison to SecondNet [13] with the same physical network scale in Figure 12, for thousands of VMs in the VDC in a fat-tree topology, SecondNet performed allocation in 20-90 s, while GANA-VDC only took 0.41-0.48 s. The results show GANA-VDC allocation time cost was far less. **Mappings Duplication:** In our experiments, more than 70,000 active mapping sets consisted of the mapped physical switches. Statistically, we analyzed the number of VDC allocations with the same mapping sets, about 40,550; thus, we computed the duplication degree of the VDC allocations, about 42.49% on average. With our proposed VDC allocation algorithm, we did not take this factor into consideration, and, to some extent, the results prove our VN topology discovery optimization to be more beneficial. Figure 11: VDC Allocation time. (**a**) Linear Fitting process on VDC allocations time; (**b**) Moving Average process on VDC allocations time

Figure 12: Comparison to SecondNet [13]. ## 6 Discussion

For us to achieve desirable application-aware networking, we implemented and extended techniques proposed from previous works. FlowVisor [10] and FlowN [45] were used to provide a network virtualization layer to allow multiple isolated logical networks each with potentially different addressing and forwarding mechanisms to share the same physical infrastructure [46]. We extended them by adding an interceptor in VN responsible for source routing in a scalable large 2-layer network. For the provisioning of fine-grained traffic control, we employed the original OpenFlow QoS pattern by giving a VDC limited flowtable. We also optimized the topology discovery measure in large-scale datacenters containing many VDCs, combining inherently sending and receiving LLDP messages resorting to other VDCs. Hence, we effectively balanced the load from the per-tenant topology discovery. Tenants can build arbitrary VN topology architecture just as they used to construct individual networks. We propose the GANA model, describing VDC using its resource demands, in addition to VN and VMs. Contrary to other models, it has superior advantages such as unprecedentedly supporting multipath VN, and self-defined link bandwidth more precisely benefits pay-as-you-go business model in cloud computing. Based on the GANA model, we scheme a novel switch centered VN to PN mapping algorithm. Contrary to previous heuristic VN to PN mapping algorithms, it can be deemed as parallel and coupled with optimization to dedicate fat tree topology, thus resulting in remarkable speed. While the current technology seeks to resolve the challenge of Virtual network embedding as the ultimate solution, different disciplines are being developed to improve the services. This was documented by [PERSON] et al., who developed an interest in the future work on distributed VNE. This is in a bid to resolve the Intra-InP VNE challenge. While the format resolves the challenge of a centralized approach, it faces the challenge of being optimal in resource usage.