In cases of catastrophic events such as natural disasters or physical calamities, current network infrastructure can become inoperative. Furthermore, there are transient events leading to excessive demand surges where it is needed to deploy additional network capacity on-demand. In such cases, rapid network deployments become vital to establish communications and enable networked services. Unmanned Aerial Vehicle (UAV) networks are good candidates for this kind of operation. Software-defined networking and content-centric operation are promising technologies to enable agile control, network visibility and efficient content delivery via centralized optimization in these challenged systems. In this work, we consider an edge network which is composed of UAVs and serves in a contentcentric mode with in-network caching and device-to-device (D2D) transmissions. We develop a cache placement and selection scheme for energy-efficient operation. We also investigate how such a system performs under different operating conditions.

With the growing number of IoT (Internet of Things) devices and their particular characteristics compared to traditional systems, incumbent security mechanisms need to be advanced for secure and resilient IoT operation in current ICT systems. One particular standard, which tries to improve IoT security in that regard, is the Manufacturer Usage Description (MUD) by IETF. In this paper, as our main focus is to highlight the security gains of using MUD, we first discuss the critical threats to IoT devices based on available research. In the second step, we analyze the MUD technology to delineate where MUD is beneficial (or not) to address these security issues.

In this work, we present Graph Based Liability Analysis Framework (GRALAF) for root cause analysis (RCA) of the microservices. In this Proof-of-Concept (PoC) tool, we keep track of the performance metrics of microservices, such as service response time and CPU level values, to detect anomalies. By injecting faults in the services, we construct a Causal Bayesian Network (CBN) which represents the relation between service faults and metrics. The constructed CBN is used to predict the fault probability of services under given metrics which are assigned discrete values according to their anomaly states.

Softwarized services in converged networks are evolving from monolithic applications to distributed architectures, often comprising numerous microservices. At the same time, with the massive proliferation of IoT devices, much more complexity and diversity are added to such critical infrastructures. In that regard, Root Cause Analysis (RCA) is an important part of a running distributed service ecosystem to keep the applications available and manageable by nding the root causes of errors and malfunctions. This paper provides a topology graph based anomaly detection and RCA solution for the microservice architecture in edge-to-cloud environments entailing microservices in combination with IoT.

Under demanding operational conditions such as traffic surges, coverage issues, and low latency requirements, terrestrial networks may become inadequate to provide the expected service levels to users and applications. Moreover, when natural disasters or physical calamities occur, the existing network infrastructure may collapse, leading to formidable challenges for emergency communications in the area served. In order to provide wireless connectivity as well as facilitate a capacity boost under transient high service load situations, a substitute or auxiliary fast-deployable network is needed. Unmanned Aerial Vehicle (UAV) networks are well suited for such needs thanks to their high mobility and flexibility. In this work, we consider an edge network consisting of UAVs equipped with wireless access points. These software-defined network nodes serve a latency-sensitive workload of mobile users in an edge-to-cloud continuum setting. We investigate prioritization-based task offloading to support prioritized services in this on-demand aerial network. To serve this end, we construct an offloading management optimization model to minimize the overall penalty due to priority-weighted delay against task deadlines. Since the defined assignment problem is NP-hard, we also propose three heuristic algorithms as well as a branch and bound style quasi-optimal task offloading algorithm and investigate how the system performs under different operating conditions by conducting simulation-based experiments. Moreover, we made an open-source contribution to Mininet-WiFi to have independent Wi-Fi mediums, which were compulsory for simultaneous packet transfers on different Wi-Fi mediums.