Edge intelligence for service function chain deployment in NFV-enabled networks

With evolution of network function virtualization (NFV), network services can be provided as service function chains (SCs), each consisting of multiple virtual network functions (VNFs). The deployment of SCs including placement of VNF instances and virtual links connecting these functions, onto the substrate physical network is a critical issue which significantly affects the performance of the offered network services. Due to the unpredictable traffic and network state variations, as well as diverse quality of service (QoS) requirements, an online SCs deployment approach is needed to cope with different service requests and real-time network traffics. In this paper, we employ edge intelligence using a distributed deep reinforcement learning approach to deploy SCs in order to jointly balance the load on the physical nodes and links in the edge environments. The evaluation results show that the proposed approach outperforms state-of-the-art algorithms in terms of minimizing the drop rate of the incoming service chain requests. In addition, the proposed approach is able to rapidly deploy service flows even in the large real-world network typologies

Mechanical Characterization of Torsional Micropaddles Using Atomic Force Microscopy

The reference cantilever method is shown to act as a direct and simple method for determination of torsional spring constant. It has been applied to the characterization of micropaddle structures similar to those proposed for resonant functionalized chemical sensors and resonant thermal detectors. It is shown that this method can be used as an effective procedure to characterize a key parameter of these devices and would be applicable to characterization of other similar MEMS/NEMS devices such as micromirrors. In this study, two sets of micropaddles are manufactured (beams at centre and offset by 2.5 μm) by using LPCVD silicon nitride as a substrate. The patterning is made by direct milling using focused ion beam. The torsional spring constant is achieved through micromechanical analysis via atomic force microscopy. To obtain the gradient of force curve, the area of the micropaddle is scanned and the behaviour of each pixel is investigated through an automated developed code. The experimental results are in a good agreement with theoretical results

The relationship of social support and quality of life with the level of stress in pregnant women using the PATH model

Background: Lack of adequate social support, stress, and generally poor quality of life during pregnancy leads to adverse pregnancy outcomes for both the mother and the baby. Objectives: This study aimed to investigate the relationship of social support and quality of life with level of stress during pregnancy. Materials and Methods: This was a descriptive-correlative study conducted on 210 pregnant women (meeting study criteria), attending Shahriar Social Services Hospital during 2012. Purposive convenient sampling was used. Study subjects completed questionnaires of obstetrics and demographics, VAUX social support, World Health Organization quality of life, and stress during pregnancy. Data were analyzed with SPSS-19 and Lisrel 8.8, utilizing statistical path analysis. Results: The final path model fitted well (CF1 = 1, RMSEA = 0.00) and showed that direct quality of life paths with β = -0.2, and indirect social support with β = -0.088 had the most effects on reduction of stress during pregnancy. Conclusion: Social support indirectly and quality of life directly affect stress during pregnancy. Thus, health officials should attempt to establish measures to further enhance social support and quality of life of pregnant women to reduce stress and its consequences during this time. © 2013, Iranian Red Crescent Medical Journal

Federated learning for distributed intrusion detection systems in public networks

Abstract. The rapid integration of technologies such as IoT devices, cloud, and edge computing has led to a progressively interconnected network of intelligent environments, services, and public infrastructures. This evolution highlights the critical need for sophisticated and self-governing Intrusion Detection Systems (IDS) to enhance trust and ensure the security and integrity of these interconnected environments. Furthermore, the advancement of AI-based Intrusion Detection Systems hinges on the effective utilization of high-quality data for model training. A considerable number of datasets created in controlled lab environments have recently been released, which has significantly facilitated researchers in developing and evaluating resilient Machine Learning models. However, a substantial portion of the architectures and datasets available are now considered outdated. As a result, the principal aim of this thesis is to contribute to the enhancement of knowledge concerning the creation of contemporary testbed architectures specifically designed for defense systems. The main objective of this study is to propose an innovative testbed infrastructure design, capitalizing on the broad connectivity panOULU public network, to facilitate the analysis and evaluation of AI-based security applications within a public network setting. The testbed incorporates a variety of distributed computing paradigms including edge, fog, and cloud computing. It simplifies the adoption of technologies like Software-Defined Networking, Network Function Virtualization, and Service Orchestration by leveraging the capabilities of the VMware vSphere platform. In the learning phase, a custom-developed application uses information from the attackers to automatically classify incoming data as either normal or malicious. This labeled data is then used for training machine learning models within a federated learning framework (FED-ML). The trained models are validated using previously unseen network data (test data). The entire procedure, from collecting network traffic to labeling data, and from training models within the federated architecture, operates autonomously, removing the necessity for human involvement. The development and implementation of FED-ML models in this thesis may contribute towards laying the groundwork for future-forward, AI-oriented cybersecurity measures. The dataset and testbed configuration showcased in this research could improve our understanding of the challenges associated with safeguarding public networks, especially those with heterogeneous environments comprising various technologies

Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure

On graphs whose star sets are (co-)cliques

AbstractIn this paper we study graphs all of whose star sets induce cliques or co-cliques. We show that the star sets of every tree for each eigenvalue are independent sets. Among other results it is shown that each star set of a connected graph G with three distinct eigenvalues induces a clique if and only if G=K1,2 or K2,…,2. It is also proved that stars are the only graphs with three distinct eigenvalues having a star partition with independent star sets

Association of Maternal Working Condition with Low Birth Weight: The Social Determinants of Health Approach

Background: The socioeconomic conditions have made more job opportunities available to women. This has created interest to conduct studies on the effect of working lifestyle on pregnancy outcomes. Aim: This study was conducted with the aim to assess the relationship between mothers’ working status as a social determinant and the incidence of low birth weight (LBW) of the newborn. Subjects and Methods: This case–control study was conducted on 500 women with normal weight infants (control group) and 250 women with LBW infants (case group) in selected hospitals in Tehran. Data were collected using a researcher‑made questionnaire, designed to assess the effect of mothers’ prenatal lifestyle, as a social determinant, on LBW of the newborn. A section of the questionnaire involved assessment of mother’s working condition in terms of the work environment, activities, and job satisfaction. Data were analyzed using Chi‑square and logistic regression tests. Results: LBW among employed mothers was 5 times more likely than unemployed ones (odds ratio = 5.35, P < 0.001). Unfavorable work conditions such as humid environment, contact with detergents, and being in one standing or sitting position for long hours were significantly associated with LBW (P < 0.001). Conclusion: The present study showed that unfavorable work conditions were associated with LBW; therefore, they need special attention.Keywords: Low birth weight, Pregnancy, Socioeconomic factors, Working condition