# GANA-VDC: Application-Aware with Bandwidth Guarantee in Cloud Datacenters
## 2 Related work
Virtual switches remain interconnected, similar to practical commodity switches, providing limited interfaces link VMs and the corresponding restricted bandwidth, forming the network structure. **Application-aware Functionality:** As more applications migrate to the cloud and best effort QoS no longer entails a satisfactory approach, a new breed of intelligent networks is necessary. Application-aware networking is the answer. The networks need to sophisticatedly delve deeper into tenant-applications to enhance high performance. The ability to benefit from application-awareness depends upon SDN, particularly separating the data plane from the control plane, and thus facilitates on the fly network architecture reconfiguration and dynamic control of efficient resource allocation [28]. This opens application-aware networking making VDC-based application-aware networking a crucial and attractive cloud datacenter approach. **Tenants Oriented QoS:** Cloud data centers share environments with a high multiplex level to benefit from cost-efficiency and scale where the same cluster allows for tasks from various tenants and VMs to coexist [29]. Cloud data centers have strong requirement to enforce performance isolation among tenants, but the current existing mechanisms fall short on data center network I/O resources, as performance predictability remains a crucial concern for customers assessing a transfer using cloud data centers. Cloud data centers offer tenant-oriented QoS support, including providing bandwidth guarantee to insulate inter VDCs and enabling fine-grained traffic control mechanisms for intra-VDC. **Quality of Experience (QoE):** Cases where security systems contribute to the reduced or poor performance of a system are common. Often, developers forego quality as opposed to having security threats. A proposal by [PERSON] et al. [30], however, seeks to resolve this challenge [30]. They proposedan intrusion detection system that does not compromise the user requirements of quality of service (QOS) and quality of experience (QOE). This is achieved by providing a dedicated connection with specific capabilities. According to the researchers, this is achieved by creating clusters that provide specific services. The cluster heads will then communicate with trusted third-party entities (TTPs), which will ensure the system remains safe. The researchers' framework is based on detecting intrusive nodes through Deep belief and Decision Tree-based Hybrid Intrusion Detection System. **Security:** Several security threats affect bandwidth allocation or rather manipulate the bandwidth allocation. One major case is the Black-Hole (B-H) attack. This is case where a node receiving data packets drops or blocks the data packet as opposed to sending it the next recipient or receiving node. This threat was highlighted by [PERSON] et al. [PERSON] et al. (2018). The authors proposed a system that evaluates the validity of a node based on its selfishness, energy, honesty and intimacy. This is achieved by detecting the B-H cluster heads, where re-clustering is done to ensure the legitimate cluster heads (CHs) are in use. The process of re-clustering is purely based on a trust score, which, according to the trio, is the best way of distinguishing between normal and malicious devices. In their work, the authors used a hierarchical trust score evaluation method [PERSON] et al. (2018). The authors researched on the performance of different thresholds divided into 20%, 30%, 40% and 50%. At 50%, the system was most stable while at 20% it was most vulnerable with all the CHs being considered as B-H nodes. **Data Aggregation**: In [PERSON] et al. (2018), the authors worked on a system capable of recognizing intrusion for wireless sensor clusters in critical infrastructure. Sensory data from other sensors in the corresponding cluster are aggregated in the cluster head. This information is then sent to a centralized sink. [PERSON] et al. confirmed that data aggregation is used to measure the aggregator's trust score.