# Blockchain-Assisted QoS-Aware Routing for Software-Defined Wide Area Network
## 2 Related Work  
Software-Defined Networking (SDN) has emerged as a prominent networking architecture due to its attractive strategy of decoupling the software-driven control plane (i.e., SDN controller) from the data plane (i.e., SDN switches) that allows service providers, with greater flexibility, to programmatically manage their network infrastructure. The SDN controller generally maintains flow tables to handle the network flows, but some of the flows may require special consideration (e.g. QoS-related events) that need to be actively monitored. The SDN controller can instruct SDN switches to directly monitor those specific flows. According to [PERSON] et al. [8], assigning such tasks to a switch may be effective if those specific events occur frequently or periodically but can create overheads on the switch in case of rare occurrences of those events. They highlighted the need for intelligent monitoring mechanisms to avoid inefficiencies and proposed an efficient QoS-aware flow management in SDN-based monitoring. In an SDN, when a switch receives a new packet that does not match any existing rules in its flow table, it forwards the request to the SDN controller. Based on the current network view, the controller determines the appropriate path for the new flow. In distributed SDN environments, each controller handles flows within its local domain or delegates them to another controller's domain (if appropriate). The controllers periodically synchronize among themselves by sharing updated network states, which is generally termed as a passive monitoring strategy. In contrast, an active monitoring strategy involves network state information collection through real-time interactions rather than passively waiting for regular updates. [PERSON] et al. [9] made a tradeoff between active and passive strategies under varying conditions, such as traffic variability, network scale, etc., and, through experiments and using mathematical models, they suggested that passive state collection is better where there is low variation in traffic. Transitioning legacy infrastructure to an SDN-based architecture is typically an incremental process, often requiring the coexistence of SDN-enabled and legacy (non-SDN) devices within the same network. The gradual upgrade of legacy infrastructure to an SDN introduces heterogeneity in packet forwarding behavior, which can negatively affect the overall performance of SDN-based routing strategies. [PERSON] et al. [10] addressed this challenge by examining various forwarding strategies in hybrid SDN environments. Their work proposed a solution to effectively manage the heterogeneity caused by the presence of both legacy routers and SDN-enabled devices, thereby enhancing the routing performance and providing control over hybrid networks. [PERSON] et al. [11] described that an SDN often uses routing protocols like Open Shortest Path First (OSPF) for intra-domain routing, where a single metric routing based on hop count or link cost may not always perform optimally. As a result, this may lead to multiple data flows converging on the same link, and such convergence can cause congestion and inefficient resource utilization. To address this, the authors suggested routing paths based on diverse QoS requirements, such as low latency, minimal jitter, and high throughput, tailored to the requirements of individual data flows. Unlike traditional approaches, the authors also used deep Reinforcement Learning (DRL), specifically a Double Deep Q-Network (DDQN), to optimize QoS-aware routing in software-defined heterogeneous networks. Their proposed approach can predict traffic patterns successfully to make real-time routing decisions in bandwidth-constrained environments. Similarly, [PERSON] et al. [12] proposed an intelligent routing mechanism (QI-RM) for an SDN to meet the diverse QoS requirements of IoT data flows. They introduced a machine learning-based classification method (MACCA2-RF&RF) to characterize flow types that can identify QoS needs with high accuracy.