# Performance Analysis of Storage Systems in Edge Computing Infrastructures
## 2 Related Work
Depending on the priorities of the researchers, two of the most important fields of interest in the relevant literature regarding edge storage architectures are security and resource efficiency [30]. In most of the cases, these two priorities are in direct conflict, because in order to improve the resource efficiency, some security rules need to be relaxed, and in order to improve the security, more resources need to be committed. For example, in systems that are based on blockchain and cryptographic security controllers, a great deal of middleware and network orchestrators are needed, allowing the framework to perform the necessary encryptions, decryptions, and security checks on each data transaction [31; 32]. Some of the work performed in secure edge storage architectures prioritizes a different set of data security goals, such as availability and integrity. These approaches require a high redundancy which, again, is creating resource-demanding platforms [33; 34; 35]. Both erasure coding and data replication, which are the most common methodologies for ensuring availability and integrity, require additional nodes that are tasked with holding the replicated data and coordinating the data reading and recovery efforts. On the other hand, the systems that focus on high resource efficiency are usually bypassing data security altogether, focusing only on the data transfer and storage between the nodes, not taking into account the resources needed to secure the data packets transferred through the internet or the communication links between the nodes of the edge network [36; 37; 38]. These networks are often designed and evaluated with the assumption that data and network security are handled in another level of the data transfer and storage that is just out of their scope. Despite the fact that security is a major issue in every IoT system, cyber-risk regulations and assessment are still in their infancy. For that reason, the authors in [39] presented an analysis of cyber-risk assessment approaches in complex IoT systems and developed an epistemological analysis that enables the assessment of uncontrollable risk states in such systems. The performance of IoT active devices can be improved by sharing their communication and computation resources. However, most works in the literature focus on either communication cooperation or computation cooperation. In [40], the authors proposed an energy-efficient resource allocation scheme in a wireless-powered MEC system, by leveraging a joint communication/computation cooperation among users. This joint strategy has been proven to reduce overall energy consumption compared to other state-of-the-art works. As far as QoS is concerned, it is difficult for users to select the services with the highest quality. Over the years, many studies have been conducted for QoS prediction in edge computing environments. In [41], the authors proposed a QoS prediction approach by employing and extending the ARIMA model. Finally, in [42], Vehicular Edge Computing (VEC) is presented as a mechanism for improving the QoS, where a volunteer-assisted model is utilized for computation offloading.