# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
## 2 Related Work  
This section lays out a literature review of the proposals sharing some commonalities with ours. Because the pillars of our work are SDN and eINP for enabling application-aware network traffic management in MEC-integrated industrial environments, we will discuss where edge computing, SDN, and INP (or a combination of them) are (jointly)adopted in the literature. In this regard, we will first explore how industrial environments take advantage of edge computing. Then, we will investigate edge computing and SDN to support IIoT contexts. Subsequently, we will delve into the realm of INP-based proposals. Finally, we will conclude by pointing out the novelty of our work as compared to the reviewed literature. Industrial environments adopt edge computing more and more frequently with the primary goal of bringing computation close to data sources, i.e., from the cloud to on-premises [10; 11]. This may improve, among other things, data protection (by processing sensitive data on-premises rather than in the cloud) and real-time responsiveness (because latency at the edge is much lower than at the cloud). In this regard, ref. [12] proposes an architecture to support time-sensitive tasks and real-time data analysis at the edge whereas big data analytics tools reside in the cloud. The European Telecommunications Standards Institute (ETSI) has defined MEC as a \"system which provides an IT service environment and cloud-computing capabilities at the edge of an access network which contains one or more type of access technology, and in close proximity to its users\" [13], provided a framework and reference architecture for it [14; 15], detailed its technical requirements [16], and explored its integration with 3 GPP 5G [17]. The role of MEC has also been explored in several industrial verticals [18]. For example, ref. [19] designs a multilevel MEC-compliant computing architecture that enables the unified management of fog/edge-computing resources in Industry 4.0. Instead, ref. [20] proposes a multitier MEC platform to meet the demanding requirements of IIoT applications and explores its pros and cons. The adoption of edge computing and SDN [21; 22] is gaining momentum. For instance, ref. [23] adopts SDN to handle the edge-cloud interplay in IIoT environments. Specifically, the authors relied on SDN to handle big data streams in an energy-aware and QoS-guaranteed fashion. Similarly, ref. [24] combines edge computing with SDN to support data streams with different latency constraints among IIoT devices. The SDN paradigm turned out to be effective even in wireless ad hoc scenarios, which are typically characterized by node mobility and heterogeneity [25]. For example, ref. [26] provides high-reliability and low-latency vehicle-to-everything communications by joining vehicular ad hoc networks and wireless cellular networks, wherein base stations are equipped with MEC technology. Instead, ref. [27] proposes an intelligent multiattribute routing scheme based on fuzzy logic, whereby buses and vehicles act as relay nodes. Recently, INP, also known as in-network computing, has been proposed to support the execution on NDs of software modules that typically run on end hosts [28; 29]. However, its adoption in industrial environments has not yet been widely investigated. A notable first proposal is [30], which proposes an SDN-based adaptive transmission protocol to support time-critical services by on-demand activating in-network functions for in-path caching and retransmission. An example of making edge computing and INP work together is found in [31], which offloads critical lightweight (sub)tasks to NDs while keeping the others on edge-computing resources. Some INP-related proposals already provide forms of payload processing directly within NDs but with a limited degree of expressiveness. For instance, ref.