# GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters
## 4 GANA-VDC System Architecture
### Mapping Procedure
With these three phases, we complete the overall VN to PN mapping procedure. Note that we consider the factor of even VDC allocation in all the mapping phases. Within the first phase, we sort the pods in ascending order in accordance with the VMs that can be accommodated. The pods are selected in a greedy approach. Through Phases 2 and 3, we abstract the corresponding mappings into the min-cost network flow problem. By making every partial mapping even, we achieve overall fair balanced VDC allocation, achieving higher network utilization. ## 5 Experiments and Evaluation

In our experiment, we evaluated the performance of our VDC allocation mechanism, conducting experiments on Dawning server eight quad-core AMD 800 MHZ CPU and 32G RAM. In this experiment, a simulator with three-level fat-tree topology with 48 port 10 Gig Ethernet commodity switches was used. In this simulation, there were 27,648 physical nodes and 24 VMs accommodated per host, resulting in 663,552 virtual machines. All applications requiring guaranteed bandwidth were analyzed using statistics. Five possible outcomes of this analysis allowed us to categorize them into typical scenarios for applications. Note the assumption were made to choose the most suitable VN topology for each of these applications in an empirical fashion. These were chosen as templates to help mimic and create VN topology. Web-based application (Figure 9A): Multi-tier application forms the majority of the web-based applications [42]. Typically, three tiers exist: front-end web-based server, medium application server, and the back-end database. Bandwidth guarantees favor the middle-to-backend and frontend-to-middle to assure a predictable performance while servicing web services tenants. Enterprise networks (Figure 9B): Companies purchase cloud services to automate typical office work. The majority of middle-size companies have hundreds of employees divided into departments; therefore, thousands of hosts are needed. Generally, an enterprise network with oversubscription will benefit from using a three-layer model. Storm (Figure 9C): The application for real-time data analytics. Storm [43] is used for ML on the web with a non-interrupted calculation of data streams. These kinds of applications are composed of two Java thread implemented parts: \"bolts\", which represents both a mapper and reducer; and \"spouts\", analog to the mappers used in MapReduce [44]. The traffic among components is considered exceedingly directional. MapReduce (Figure 9D): Many infrastructure services demand non-blocking network services such as MapReduce [44]. Intermediate files need to be fetched from various servers when performing the Reduce operation phase in the MapReduce mechanism. The traffic forms an all-to-allcommunication pattern the Reduce workers generate, thus highlighting the need for the specific network topology a fat-free topology offers. File backup service (Figure 9E): The characteristic of this service mostly reflects downward traffic. Table 2 describes the proportion accounting for each application in our experiments. We did not consider the CPU intensive applications, as we assume they did not impose a noticeable impact on networking. Synthetic VDC application types were generated, where the ratio occupied by each application decided the optimal way to distribute it. Furthermore, the size of the VDC was randomized, and the weight for the VN topology was assigned. We chose different ranges and empirical estimates based on variance with the chosen template. The network utilization, given by (_network_util_), demonstrates how the VDCs were divided by the total link capacity, while (_server_util_), i.e., the server bandwidth utilization, corresponds to the same bandwidth but distributed via server link capacity. Finally, pod bandwidth utilization, (_pod_util_), follows the same definition but with the pod capacity as the dividing factor.