Kubernetes (K8s) has become the cornerstone solution for orchestrating containerized applications across cloud-edge computing environments. Container Network Interface (CNI) plugins are crucial in this ecosystem, enabling network connectivity among K8s components, supporting advanced features and facilitating robust network management across diverse environments, such as centralized cloud or decentralized edge locations. In this paper, we extend our previous work, focusing on the various features, either built-in or non-native, provided within each networking solution and across diverse resource-intense and lightweight K8s distributions. Along these lines, tunneling options, security and encryption mechanisms, as well as common service mesh and observability tools have been evaluated with regards to their performance and resource utilization.

Kubernetes (K8s) serves as a mature orchestration system for the seamless deployment and management of containerized applications spanning across cloud and edge environments. In this context, optimizing Kubernetes networking to achieve high-performance connectivity and minimal resource utilization is crucial for its applicability and effectiveness at the edge. This paper contributes to this effort, by conducting a qualitative and quantitative performance evaluation of diverse Container Network Interface (CNI) plugins within different K8s environments, incorporating lightweight implementations designed for the Edge. Our experimental assessment was conducted in two distinct (intra- and inter-host) scenarios, revealing interesting insights and tradeoffs for both researchers and practitioners. For example, the deployment of plugins across lightweight distributions does not necessarily lead to resource utilization improvements, e.g., in terms of CPU/memory or throughput, while, in contrast, their impact on lifecycle metrics such as pod readiness times, is significant.

Smart cities are progressively emerging and require city-wide networks able to maintain low delay and increased reliability. However, wireless communications in the urban environment are occasionally disrupted, leading to volatile network conditions and impeding traditional networks to cope with these demands. To this end, Named Data Networking (NDN) and Delay/Disruption Tolerant Networking (DTN) architectures, as well as the combined NDN-over-DTN (NoD) scheme have been introduced and are actively investigated for combating distinct communication challenges in wireless city-wide deployments. In this work we investigate the performance and reliability trade-offs of each approach in the smart city context, and accordingly propose an experimental system that employs interchangeably NDN, DTN and NoD driven by a novel Change Point (CP) based network reliability detector. The real-world experiments showed that the proposed multi-protocol adaptive system, called ALTER, leads to lower latency and increased reliability of the urban network, compared to non-adaptive NDN and NoD solutions.

In this paper, we devise preprocessing schemes to disentangle channel state information (CSI) into predictable and unpredictable components to simultaneously provide two cornerstone security operations. The predictable components are used for node authentication and the unpredictable components for secret key generation (SKG). For the case of SKG, to prevent Eve from exploiting potential spatial, frequency or time correlations with the legitimate users, which would reduce the effective key space through a decrease in the brute force attack size, in this work, we emphasise the need for reducing the spatial correlation (SC) at different transmitter locations. We also study the trade-off between SC and reconciliation in the uplink and the downlink. Furthermore, we discuss the importance of a more robust criterion - independence - over decorrelation between the legitimate users and eavesdroppers. Finally, we propose a metric for quantifying uniqueness in the predictable components for node authentication, using the total variation distance (TVD).

We propose an SDN-based Non-IP multi-protocol platform (REWIRE) that mixes and matches, on-demand, multi-ple Non-IP protocol strategies with real rapidly-detected network conditions and loT data communication patterns. The REWIRE solution supports centralized monitoring of Wireless Mesh Net-works (WMN) and manages alternative Non-IP protocols stacks (i.e., NDN, DTN & NoD) in an adaptable manner based on change-point analysis & clustering mechanisms. To this end, our proposal grafts flexibility and adaptability capabilities to the WMN providing the communication backbone in real Smart-City environments. Our platform was implemented in real WMNs environments over Fed4FIRE+ test-beds considering an loT sce-nario using traffic patterns based on real sensor measurements.

Video content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. For flexible and highly adaptive solutions, the capability for a quick response should be driven from early (real-time) and low-complexity content popularity detection schemes. In this paper, we focus on the early and low-complexity detection of video content popularity, which we address as a statistical change point (CP) detection problem. Our proposed methodology estimates in real-time the existence, the number, the magnitude and the direction of changes in the average number of video visits by combining: (i) off-line and on-line CP schemes; (ii) an improved measurements window segmentation heuristic for the detection of multiple CPs; and (iii) a variation of the moving average convergence divergence (MACD) indicator to detect the direction of changes. We evaluated the proposed framework using a large database of real youtube video visits. The proposed algorithm is shown to accurately identify CPs and the direction of change in the off-line phase. Finally, a few illustrative examples of two variations of the on-line algorithm are also included.