# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 2 Related Work  
Finding efficient routes to achieve the desired SDN performance leads to facilitating the processing and decreasing the end-to-end delay. SDN traffic is measured and analyzed in order to enhance the performance of an operational network at both the traffic and resource levels. Recently, several works are done in routing over the SDNs [1]. Several recent works have exploited the global visibility offered by SDN controllers to distribute traffic across pre-computed paths in optimal and suboptimal manners such as Software-driven wide area network (SWAN) [2] and B4 [3] that the former one uses k-shortest paths for routing across the SDN switches while the latter one presents a greedy heuristic to ensure fairness; however, both of them lack sufficient flexibility and path diversity to handle unexpected situations, i.e., traffic bursts, link failures. Recently, authors in [4; 5] tackled models (e.g., oblivious routing for wide-area network traffic engineering [6; 7]) and estimate traffic tools in order to cope with these problems. In detail, authors in [5] as a power routing toolbox presents several failure scenarios, path budgets, to simulate a variety of realistic workloads and compare the performance of the existing routing on the \"failed\" network, to the best possible routing on the failed network. From the SDN load balancing point of view, the literature presents load balancing technologies for data and control layer traffic in order to centralize multiple optional link utilization rates and flow characteristics. For example, Authors in [8] present a routing algorithm that manage and optimize traffic in the SDN using an additional backend server. Moreover, another similar solution is [9] that comprises routing optimization that deals with various shapes of incoming flows using a heuristic equal-cost multi-path routing (ECMP) algorithm to monitor, manage and route the aggregated flows. Although these methods are interesting, they did not cover the adaptive rerouting in such a manner that recovers the current situation of the SDN when faced with link/network congestion. In [10], authors present user aspect overview functions through the SDN architecture by experiencing the desired performances of requests such as suitability and applicability. This holistic architecture has the flavor of useful practical concepts, but it suffers from reliability and availability when faced with link and network congestion. Some other routing methods target the quality of services (QoSs) over such networks. The SDN provides an open control interface to support QoS requirements and preserves flexible network traffic strategies to satisfy different network applications. A QoS-aware network reconfiguration for software defined networks is described in [11]. They reallocate the resources in a way that minimizes the network reconfiguration overhead. Similarly, The papers [12; 13] propose two rerouting algorithms to guarantee the QoS constraints while the energy consumption of the network is minimized. In this way, they provide a mathematical formulation of the problem. In addition, authors in [14] present a novel network function virtualization (NFV)/SDN orchestration between Internet of things (IoT) gateways and the deployed VMs that are allocated to the edge node. The presented paper tested in terms of packet delivery for VMs in various cases. The paper and the results are interesting but after deep analysis, we realized that the work did not address the traffic prediction and various QoS requirements that matter in QRTP. The work [15], proposes a traffic engineering algorithm that focuses on improving data center network utilization. In [16; 17; 18; 19], the authors reroute video flows in order to minimize delay and packet loss of total traffic. They guarantee that the end-to-end delay of each flow is less than a predefined threshold. [PERSON] et al. [20] propose a video streaming resource allocation algorithm, with two different classes, which minimize both selected path hops and packet loss.