Enabling P4 Network Telemetry in Edge Micro Data Centers With Kubernetes Orchestration

Integrating computation resources with networking technologies is an hot research topic targeting the optimization of containers deployment on a set of host machines interconnected by a network infrastructure. Particularly, next generation edge nodes will offer significant advantages leveraging on integrated computation resources and networking awareness, enabling configurable, granular and monitorable quality of service to different micro-services, applications and tenants, especially in terms of bounded end-to-end latency. In this regard, SDN is a key technology enabling network telemetry and traffic switching with the granularity of the single traffic flow. However, currently available solutions are based on legacy SDN techniques, not enabling the matching of tunneled traffic, and thus require a tricky integration inside the hosts where containers are deployed. This work considers Kubernetes clusters deployed on next generation edge micro data center platforms and proposes an innovative SDN solution exploiting the P4 technology to gain visibility inside tunnelled traffic exchanged among pods. This way, the integration is achieved at the control plane level through the communication between Kubernetes and the SDN controller. The proposed solution is experimentally validated including a comprehensive framework enabling effective traffic switching and in-band telemetry at pod level. The major paper contributions consist in the design and the development of: (i) the networking applications at SDN control plane level; (ii) the P4 switch pipeline at the data plane level; (iii) the monitoring system used to collect, aggregate and elaborate the telemetry data

Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator

Network emulation offers a flexible solution for network deployment and operations, leveraging software to consolidate all nodes in a topology and utilizing the resources of a single host system server. This research paper investigated the state of cybersecurity in virtualized systems, covering vulnerabilities, exploitation techniques, remediation methods, and deployment strategies, based on an extensive review of the related literature. We conducted a comprehensive performance evaluation and comparison of two network-emulation platforms: Graphical Network Simulator-3 (GNS3), an established open-source platform, and the SEED Internet Emulator, an emerging platform, alongside physical Cisco routers. Additionally, we present a Distributed System that seamlessly integrates network architecture and emulation capabilities. Empirical experiments assessed various performance criteria, including the bandwidth, throughput, latency, and jitter. Insights into the advantages, challenges, and limitations of each platform are provided based on the performance evaluation. Furthermore, we analyzed the deployment costs and energy consumption, focusing on the economic aspects of the proposed application

Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study

This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table