Network Programming Research Articles

SDN-Controlled Resource-Tailored Analytics for Healthcare IoT System

In this work, we propose phSDN, a programmable healthcare network architecture for resource-tailored management and on-demand allocation of analytic modules in Internet of Things (IoT) healthcare system. The identification, dynamic management of services, and fast analysis of healthcare data are a few challenges in existing remote healthcare monitoring devices. phSDN programs and reconfigures the resources and services, while updating the associated flow-rules in the data-plane of software-defined network (SDN) architecture. It is especially useful in scenarios with intermittent or constrained network connectivity and/or device availability. The newly identified flows associated with different resources are explicitly considered, while forwarding services over the SDN network. The on-the-fly programmable services enable healthcare devices to provide resource-specific analytics. The programmability over services also allows unified control over the end-devices by reconfiguring on-the-fly vendor-specific heterogeneity of resource types. Our prototype of phSDN supports three different types of resources: 1) body temperature, 2) pulse rate, and 3) blood pressure. Experiments using our implementation show that the proposed architecture minimizes the network response time by at least 43.87% and 78.4%, compared to the fog- and cloud-based approaches, respectively. The implemented system's scalability is further evaluated using Mininet by considering latency, packet delivery ratio, and throughput, as the performance metrics.

Networking in Academic Medicine: Keeping an Eye on Equity.

Networking in Academic Medicine: Keeping an Eye on Equity.

P4Tune: Enabling Programmability in Non-Programmable Networks

Data plane programmability has attracted significant attention, permitting network operators to run customized packet processing functions. Unfortunately, networks today are still largely dominated by proprietary fixed-function devices. It is challenging for operators to completely migrate to programmable data planes (PDPs) due to the economical costs of replacing equipment and limited expertise in maintaining and operating programmable networks. This article introduces P4Tune, a cost-efficient architecture that uses passive PDPs coupled with optical taps. P4Tune runs customized packet processing functions operating at line rate and collects network information with nanosecond resolution. This visibility enables intelligent algorithms residing in the control plane to construct configuration rules and apply them on the legacy devices, thus creating a closed control loop. P4Tune brings the benefits of processing traffic at line rate to legacy networks. Additionally, by using PDPs passively, the architecture can be safely deployed without disrupting current network operations, fostering incremental use of PDPs (i.e., no need to deploy complex code at once). The article demonstrates three applications of the proposed architecture related to Quality of Service (QoS) and cybersecurity.

PTA: Finding Hard-to-Find Data Plane Bugs

Bugs in network hardware can cause tremendous problems. However, programmable network devices have the potential to provide greater visibility into the internal behavior of devices, allowing us to more quickly find and identify problems. In this paper, we provide a taxonomy of data plane bugs, and use the taxonomy to derive a Portable Test Architecture (PTA) which offers essential abstractions for testing on a variety of network hardware devices. PTA is implemented with a novel data plane design that (i) separates target-specific from target-independent components, allowing for portability, and (ii) allows users to write a test program once at compile time, but dynamically alter the behavior via runtime configuration. We report 12 diverse bugs on different hardware targets, and their associated software, exposed using PTA.

Toward Low-Latency and Accurate State Synchronization for Programmable Networks

Programmable switches empower stateful packet processing, in which incoming packets continuously update states in the data plane, while applications in the control plane read and write states. However, since the data plane and control plane are separated, a consistent view of states in both planes is required for stateful packet processing. Existing approaches suffer from either high latency or low accuracy. In this paper, we propose, a framework that offers approximate state synchronization with low latency and high accuracy. To achieve low latency, directly transfers states between switch ASICs and the control plane by bypassing switch operating systems. To achieve high accuracy, utilizes the resources in the switch ASIC to realize rate control in state synchronization, such that it avoids potential state loss. It also bounds the divergence between the states in the data plane and that in the control plane under limited link capacity. We prototype on Barefoot Tofino switches. The experimental results indicate that compared to existing approaches, achieves order-of-magnitude latency reduction while maintaining high accuracy of state synchronization. Also, our experiments demonstrate that provides significant latency benefits to existing network management applications and well preserves high application-level accuracy.

Demographic factors associated with time to treatment initiation in hepatocellular carcinoma.

e18546 Background: Hepatocellular carcinoma (HCC) accounts for 75% of all liver cancer diagnosis in the United States, with incidence rising in recent years. One aspect of treatment is time to treatment initiation (TTI). TTI is associated with better outcomes and lower patient anxiety. We hypothesize that demographic factors play a role in how fast a patient with HCC receives treatment. Methods: This study looked at 112,615 patients diagnosed with HCC from 2004-2019 in the National Cancer Database (NCDB). Demographic factors analyzed were biological sex, race, age, cancer stage, year of diagnosis, insurance status, household income, Charleson-Deyo score, and type of facility for treatment. Histology was determined using ICD-O-3 coding. Cross tabulation analysis was performed using ANOVA. Results: Patients with delayed TTI were more often male, black, stage III, 61-70 years old, on government insurance, diagnosed between 2016-2019, made less than $40,227, and treated at integrated cancer network programs. Patients with no insurance received a TTI 4 days faster than government insurance (p < .001). Patients with lower incomes also experienced greater than 2-day delays in TTI (p < .001) compared to higher incomes. Treatment at an integrated cancer network was associated with a 3-day delay compared to treatment at a community cancer center (p < .001). Males began treatment a half day after female patients (p < .017). Conclusions: Discrepancies exist in TTI for HCC patients, most notably for insurance status, race, year of diagnosis, income, and facility type. This data can be used when determining which patients may experience delays in TTI due to demographic factors.

Development of Learning Management System-Based Teaching Materials to Increase Students' Learning Independence

This research aims to develop appropriate teaching materials for the Vocational Fundamentals subject in Class X using the Learning Management System (LMS), with the primary goal of enhancing students' learning independence. Additionally, the effectiveness of using LMS-based teaching materials in promoting learning independence within the Computer Network and Telecommunications Engineering (TJKT) competency program is examined. The research follows a Research and Development (R&D) approach, utilizing the ADDIE development model, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. These stages provide a systematic and effective framework for developing LMS-based teaching materials. The participants in this study include teachers and students from SMKN 1, SMKN 3, and SMKN 6 in Bengkulu City. Data collection methods include expert assessments, learner independence questionnaires, and tests. The collected data will be analyzed using statistical techniques such as mean and t-tests, while expert validation will employ a Likert scale. The research findings indicate that the teaching materials developed in this study are suitable for students, and the utilization of LMS-based teaching materials has proven to enhance learner independence.

Microring-based programmable coherent optical neural networks.

Coherent programmable integrated photonics circuits have shown great potential as specialized hardware accelerators for deep learning tasks, which usually involve the use of linear matrix multiplication and nonlinear activation components. We design, simulate and train an optical neural network fully based on microring resonators, which shows advantages in terms of device footprint and energy efficiency. We use tunable coupled double ring structures as the interferometer components for the linear multiplication layers and modulated microring resonators as the reconfigurable nonlinear activation components. We then develop optimization algorithms to train the direct tuning parameters such as applied voltages based on the transfer matrix method and using automatic differentiation for all optical components.

Burnout Among Psychiatry Residents and One Program's Approach to Creating a Culture of Wellness.

Psychiatry residency training includes unique characteristics that can predispose trainees to burnout, including vicarious traumatization, prevalence of patient suicide and violence in the workplace, and social stigma surrounding mental health. For the purposes of this article, the authors examine these contributing factors and address how psychiatry residency training programs, specifically the Kaiser Permanente Oakland program, are responding to these unique challenges with wellness initiatives. Initiatives to promote wellness at Kaiser Permanente Oakland include a resident and faculty-led wellness committee, work-hour limits, reasonable call schedules, a robust mentorship program, funded social and networking events programs, and comprehensive mental health services.

EXPLORING THE LANDSCAPE OF SDN-BASED DDOS DEFENSE: A HOLISTIC EXAMINATION OF DETECTION AND MITIGATION APPROACHES, RESEARCH GAPS AND PROMISING AVENUES FOR FUTURE EXPLORATION

Over the course of time, a multitude of security solutions have been proposed in order to safeguard the Internet architecture from an extensive array of malware threats. However, the task of ensuring the security of the Internet and its associated applications remains an ongoing research challenge. Researchers persistently delve into the exploration of innovative network architectures, such as the utilization of HTTP as the narrow waist, the implementation of Named Data Networking (NDN), the development of programmable networks, and the adoption of Software-Defined Networking (SDN), with the aim of designing a more dependable and resilient network infrastructure. Among these alternative approaches, SDN has emerged as a robust and secure solution for countering malicious activities. By separating the control plane from the data plane, SDN provides an array of advantages, including enhanced manageability, improved control, dynamic rule updates, advanced analysis capabilities, and a comprehensive network overview facilitated by a centralized controller. Despite its superiority over conventional IP-based networks, SDN is susceptible to various network intrusions and encounters significant challenges in terms of deployment. The purpose of this paper is to conduct a comprehensive review of approximately 70 prominent mechanisms employed for the detection and mitigation of Distributed Denial of Service (DDoS) attacks in SDN networks. These mechanisms are systematically categorized into four main groups, namely information theory-based methods, machine learning-based methods, approaches based on Artificial Neural Networks (ANN), and other miscellaneous methods. Furthermore, the paper identifies and discusses several unresolved research issues and gaps that exist in the deployment of a secure DDoS defense solution within SDN networks. The objective of this comprehensive review is to provide valuable insights to the research community, assisting in the development of more robust and reliable DDoS mitigation solutions that are specifically tailored for SDN networks.

Distribution of genetic diversity of neotropical Biomphalaria (Preston 1910) (Basommatophora: Planorbidae) intermediate hosts for schistosomiasis in Southeast Brazil

IntroductionBiomphalaria glabrata, B. tenagophila, and B. straminea occurrence are crucial for estimating the risk of infectious human schistosomiasis in the neotropics. How different geographic sample strategies influence snail genetic diversity estimations were here investigated for three Schistosoma mansoni hosts.MethodsMitochondrial gene sequences were employed for Cytochrome C Oxidase I (COI), ribosomal RNA (rRNA) 16S, and a dataset with concatenated gene sequences (COI+16S), resulting in an improved scientific hypothesis regarding the geographical distribution of snail species. This study compared the sequences of snails from the Middle Paranapanema (MP) hydrographic basin in a geographically restricted area (inner group) to snails widely distributed across a broad geographical range in São Paulo (outer group), Brazil from 1999 to 2017. DNA sequence polymorphisms and haplotype diversity were estimated using DNAsp software. Haplotype network trees were constructed using a network program. The geographical distribution of the haplotypes was mapped using QGIS. Haplotype variation and distribution were tested for population structure using analysis of molecular variance (AMOVA).Results and discussionThe genetic diversity of B. glabrata, sampled from disconnected but geographically close freshwater collections, was partitioned into two sequence groups. The haplotype network showed that the diversity of B. straminea was more spatially partitioned than in B. tenagophila, which exhibited two population groups. The haplotype distribution pattern for B. tenagophila showed many unique and exclusive haplotypes for all three loci. AMOVA showed that genetic diversity could be high in species inhabiting small geographical areas, and a large river is not a local geographical barrier for snail migration. This study found that the survey dimensions and snail samplings influenced the genetic diversity results obtained by mitochondrial DNA molecular markers.

A REVIEW OF THE SOLUTIONS FOR AUTONOMOUS EXPOSURE OF INTRUSIONS AND MALICIOUS ACTIVITIES IN AUTOMATED NETWORKS IN THE ENVIRONMENT OF BIG DATASETS

Nowadays, networks are not fully automated due to complex and complicated issues that have not been solved for a long time. In order to bring the network closer to automation, such problems as unsupervised searches for intrusions and malicious attacks must be resolved. Modern and cutting-edge ways to hack the network, intercept traffic, and plant malicious programs appear daily, making research even more difficult. Currently, we need unique and universal ways to identify interventions in networks. New comprehensive solutions must have self-controlled systems that can work autonomously and stop intrusions, heal the system, predict new vulnerable places of the networks, and prognosticate new ways of intrusions into the software-defined networks. Furthermore, current traffic is passed in massive amounts, and data representation types vary from vendor to vendor of network devices. Such problems as storage, processing, bringing the data to the same kind of representation, and creating self-supervised learning play a tremendously significant role. This article reviews different ways of using a genetic algorithm, fuzzy logic, or more precisely, fuzzy inference, fuzzy interpolation, and fuzzy clustering. Fuzzy logic-based solutions might not only be overlooked for the exposure of intrusions and malicious activities in the networks, but they can also help with an extensive range of problems of Big Data and help to balance the data and enhance processing and sorting it. Keywords: intrusion detection system, information security, fuzzy logic, fuzzy inference, fuzzy clustering.

Study on the Mechanism of MC5R Participating in Energy Metabolism of Goose Liver.

Nutrition and energy levels have an important impact on animal growth, production performance, disease occurrence and health recovery. Previous studies indicate that melanocortin 5 receptor (MC5R) is mainly involved in the regulations of exocrine gland function, lipid metabolism and immune response in animals. However, it is not clear how MC5R participates in the nutrition and energy metabolism of animals. To address this, the widely used animal models, including the overfeeding model and the fasting/refeeding model, could provide an effective tool. In this study, the expression of MC5R in goose liver was first determined in these models. Goose primary hepatocytes were then treated with nutrition/energy metabolism-related factors (glucose, oleic acid and thyroxine), which is followed by determination of MC5R gene expression. Moreover, MC5R was overexpressed in goose primary hepatocytes, followed by identification of differentially expressed genes (DEGs) and pathways subjected to MC5R regulation by transcriptome analysis. At last, some of the genes potentially regulated by MC5R were also identified in the in vivo and in vitro models, and were used to predict possible regulatory networks with PPI (protein-protein interaction networks) program. The data showed that both overfeeding and refeeding inhibited the expression of MC5R in goose liver, while fasting induced the expression of MC5R. Glucose and oleic acid could induce the expression of MC5R in goose primary hepatocytes, whereas thyroxine could inhibit it. The overexpression of MC5R significantly affected the expression of 1381 genes, and the pathways enriched with the DEGs mainly include oxidative phosphorylation, focal adhesion, ECM-receptor interaction, glutathione metabolism and MAPK signaling pathway. Interestingly, some pathways are related to glycolipid metabolism, including oxidative phosphorylation, pyruvate metabolism, citrate cycle, etc. Using the in vivo and in vitro models, it was demonstrated that the expression of some DEGs, including ACSL1, PSPH, HMGCS1, CPT1A, PACSIN2, IGFBP3, NMRK1, GYS2, ECI2, NDRG1, CDK9, FBXO25, SLC25A25, USP25 and AHCY, was associated with the expression of MC5R, suggesting these genes may mediate the biological role of MC5R in these models. In addition, PPI analysis suggests that the selected downstream genes, including GYS2, ECI2, PSPH, CPT1A, ACSL1, HMGCS1, USP25 and NDRG1, participate in the protein-protein interaction network regulated by MC5R. In conclusion, MC5R may mediate the biological effects caused by changes in nutrition and energy levels in goose hepatocytes through multiple pathways, including glycolipid-metabolism-related pathways.

Finding Room for Indigenous Women in Educational Leadership: Why Structured Mentorship and Training Are Needed

While the educational system attempts to recover from the last few years of teacher attrition and lower enrollments in teacher education programs, there is an opportunity within adult education to both promote and mentor indigenous women in educational leadership roles by providing education, resources, and opportunities during their training. As long as indigenous women educators are still left out of academic leadership spaces, the lack of diverse voices of women in educational leadership is an issue worth exploring. A troubling lack of mentorship, support, and opportunities exacerbates the exclusion of indigenous women educational leaders. The purpose of this paper is to explore ways that adult education programs and human resource development interventions can and should help prepare indigenous women for positions in educational leadership. Educational leadership training in higher education institutions must include structured mentorship and networking programs, efforts to understand how indigenous women learn and lead, and an embracing of alternate, indigenous ways of leading.

ConPoolUBF: Connection pooling and updatable Bloom filter based SYN flood defense in programmable data planes

SYN flood attack is one of the common ways in which attackers take the advantage of TCP’s three-way handshake connection establishment to overwhelm target systems. With the emergence of programmable networks, many promising security functions have been implemented at the network layer. SYN authentication and cookie-based SYN proxy are two significant approaches recommended on programmable switches against SYN flood attacks. However, while the implementation of a cookie-based SYN proxy causes additional delays and packet drops, the SYN authentication approach increases the number of packets required to establish a TCP connection. In this study, two novel functions are implemented on programmable switches using the P4 language, and a new security solution against SYN flood attacks is proposed by combining both functions. The first function is the high-accuracy updatable Bloom filters implemented to track the state of network flows for TCP connection establishment. As the proposed data structure uses a salted input, it is more resistant to target-set coverage attacks. The second is the connection pooling function on programmable switches for requests to the backend servers. In this regard, the TCP three-way handshake is offloaded from the target systems to network switches. Upon the verification of a connection request, dynamic resource allocation is carried out from the connection pool on the P4 switch, enabling the client to connect to the server seamlessly and transparently without the need for additional packets. We implement these functions and demonstrate their feasibility as an effective defense against SYN flood.

Indigenous stories of mentorship

ABSTRACT This project aimed to share stories of six Indigenous mentees and their successes, struggles, and experiences in health science and in the Indigenous Mentorship Network Program. All participants were mentees within a mentorship network specific to their province. Mentees participated in semi-structured interviews about their experiences, which were transcribed and qualitatively coded using narrative analysis and Indigenous Storywork method. Stories were organized into motifs, or recurring narrative themes. Story vignettes based on each motif were created to express prominent experiences mentees faced. Our analysis derived motifs of struggle, overcoming, and traditional ways of learning. This project is intended to give Indigenous mentees a voice, expose areas where health education can support them, and share experiences of strength through adversity. Overall, this information can be used to educate, assist Indigenous mentorship programs, and support Indigenous mentees in health science.

Local Fractional Strong Metric Dimension of Certain Complex Networks

Fractional variants of distance-based parameters have application in the fields of sensor networking, robot navigation, and integer programming problems. Complex networks are exceptional networks which exhibit significant topological features and have become quintessential research area in the field of computer science, biology, and mathematics. Owing to the possibility that many real-world systems can be intelligently modeled and represented as complex networks to examine, administer and comprehend the useful information from these real-world networks. In this paper, local fractional strong metric dimension of certain complex networks is computed. Building blocks of complex networks are considered as the symmetric networks such as cyclic networks C n , circulant networks C n 1,2 , mobious ladder networks M 2 n , and generalized prism networks G m n . In this regard, it is shown that LSFMD of C n n ≥ 3 and G m n n ≥ 6 is 1 when n is even and n / n − 1 when n is odd, whereas LSFMD of M 2 n is 1 when n is odd and n / n − 1 when n is even. Also, LSFMD of C n 1,2 is n / 2 ⌈ m + 1 / 2 ⌉ where n ≥ 6 and m = ⌈ n − 5 / 4 ⌉ .

Optical neural network via loose neuron array and functional learning

This research proposes a deep-learning paradigm, termed functional learning (FL), to physically train a loose neuron array, a group of non-handcrafted, non-differentiable, and loosely connected physical neurons whose connections and gradients are beyond explicit expression. The paradigm targets training non-differentiable hardware, and therefore solves many interdisciplinary challenges at once: the precise modeling and control of high-dimensional systems, the on-site calibration of multimodal hardware imperfectness, and the end-to-end training of non-differentiable and modeless physical neurons through implicit gradient propagation. It offers a methodology to build hardware without handcrafted design, strict fabrication, and precise assembling, thus forging paths for hardware design, chip manufacturing, physical neuron training, and system control. In addition, the functional learning paradigm is numerically and physically verified with an original light field neural network (LFNN). It realizes a programmable incoherent optical neural network, a well-known challenge that delivers light-speed, high-bandwidth, and power-efficient neural network inference via processing parallel visible light signals in the free space. As a promising supplement to existing power- and bandwidth-constrained digital neural networks, light field neural network has various potential applications: brain-inspired optical computation, high-bandwidth power-efficient neural network inference, and light-speed programmable lens/displays/detectors that operate in visible light.

Systems of Care Approach to Improve Care for Children During Public Health Emergencies

Introduction:During public health emergencies, like COVID-19 or natural disasters, care for the pediatric population can become fragmented. Communication between systems may be challenged due to lack of relationships or infrastructure barriers such as incompatible electronic health record (EHR) systems. This can create critical, life-threatening situations for pregnant patients, infants, and children with exposures to threats. A systems-level approach was developed to guide planning efforts to improve and enhance communication and data sharing along the spectrum of care for children during public health emergencies.Method:The American Academy of Pediatrics (AAP) and the Centers for Disease Control and Prevention (CDC) gathered subject matter experts to discuss how healthcare systems and community partners could strengthen the communication within the pediatric system of care. Three primary challenges emerged: Communication, Screening and Data, and Preparedness and Planning. Action steps were identified to address these challenge areas and meet the goals of reaching diverse populations, addressing health disparities, improving collaboration between health systems and public health, implementing effective screening practices that are guided by data, and strengthening infrastructure.Results:As a result of the in-depth discussions a graphic was developed to help guide those working to improve the system of care in their communities by implementing the activities described above. Selected multidisciplinary state teams will test strategies to address these goals.Conclusion:Improving communication and data sharing in the pediatric system of care will improve care and better inform the response during public health emergencies. Building successful partnerships, such as those between health care and public health, will be critical to success. The example of the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET) program, which collects and analyzes data related to public health threats, demonstrates the strength of this approach.

A Bayesian Multi-Armed Bandit Algorithm for Dynamic End-to-End Routing in SDN-Based Networks with Piecewise-Stationary Rewards

To handle the exponential growth of data-intensive network edge services and automatically solve new challenges in routing management, machine learning is steadily being incorporated into software-defined networking solutions. In this line, the article presents the design of a piecewise-stationary Bayesian multi-armed bandit approach for the online optimum end-to-end dynamic routing of data flows in the context of programmable networking systems. This learning-based approach has been analyzed with simulated and emulated data, showing the proposal’s ability to sequentially and proactively self-discover the end-to-end routing path with minimal delay among a considerable number of alternatives, even when facing abrupt changes in transmission delay distributions due to both variable congestion levels on path network devices and dynamic delays to transmission links.