Scalability Challenges Research Articles

Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization

ABSTRACTKubernetes is an open‐source container orchestration platform, offers a comprehensive suite of features for managing containerized applications effectively. These features encompass horizontal scaling, per‐node‐pool cluster scaling and automated resource request adjustments. This research endeavors to harness these capabilities to address the limitations experienced by fog servers in edge environments, particularly those arising from restricted network connectivity and scalability challenges. In this research paper, the primary focus is on Kubernetes role of enhancing scalability, providing a robust framework for managing containerized applications. The proposed approach involves creating a predefined number of pods and containers within a Kubernetes cluster, specifically designed to efficiently handle incoming requests while optimizing CPU and memory usage. This method implements a microservice architecture for the web tier, with separate pods for the front end, back end and database, ensuring modular and scalable design. All pods communicate and integrate through REST APIs, facilitating seamless interaction and data exchange between the services. When handling web requests, the approach enables and controls both internal and external networks, ensuring secure and efficient communication. The analysis then examines the CPU and memory utilization of the pods, as well as node bandwidth, to provide a comprehensive evaluation of container scalability and performance within the Kubernetes cluster. These findings effectively demonstrate Kubernetes' capability in managing container scalability and optimizing resource utilization, highlighting its efficiency and robustness in a microservice environment.

Boosting the Performance of Alias-Aware IFDS Analysis with CFL-Based Environment Transformers

The IFDS algorithm is pivotal in solving field-sensitive data-flow problems. However, its conventional use of access paths for field sensitivity leads to the generation of a large number of data-flow facts. This causes scalability challenges in larger programs, limiting its practical application in extensive codebases. In response, we propose a new field-sensitive technique that reinterprets the generation of access paths as a Context-Free Language (CFL) for field-sensitivity and formulates it as an IDE problem. This approach significantly reduces the number of data-flow facts generated and handled during the analysis, which is a major factor in performance degradation. To demonstrate the effectiveness of this approach, we developed a taint analysis tool, IDEDroid, in the IFDS/IDE framework. IDEDroid outperforms FlowDroid, an established IFDS-based taint analysis tool, in the analysis of 24 major Android apps while improving its precision (guaranteed theoretically). The speed improvement ranges from 2.1× to 2,368.4×, averaging at 222.0×, with precision gains reaching up to 20.0% (in terms of false positives reduced). This performance indicates that IDEDroid is substantially more effective in detecting information-flow leaks, making it a potentially superior tool for mobile app vetting in the market.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

Real-Time Freeway Crash Detection Framework using Connected Vehicle Waypoint Data

Accurate and timely detection of traffic accidents is crucial for transportation agencies seeking swift responses and effective traffic management, particularly on freeways where crash severity and traffic flow disruptions are amplified. While several methods have been developed using cameras or infrastructure-mounted sensors, these approaches may face challenges in geographical scalability. To address this issue, a novel approach has been developed that leverages real-time connected vehicle trajectories at a market penetration rate of 4%–9%. The method involves extracting five key features from individual journeys that sense the speed and acceleration fluctuations. The results demonstrate promising outcomes, achieving an overall accuracy of 63.5% with 4.1% false detections and 33.4% non-detections. This accuracy surpasses the speed-alone model using the same data by 24%. Furthermore, higher traffic volumes lead to even greater accuracy, exceeding 80% at level of service D and 90% at level of service E. The developed algorithm also exhibits precision in crash detection, with a mean latency of only 2.5 min after the actual incidents. The entire processing time may additionally add up to 1 min. The robustness of the model is tested for a single day on one of the interstates, providing promising results. This study highlights the potential of connected vehicle data in enhancing crash prediction methods and underlines its value for the safety and efficiency of future transportation systems.

Harnessing the Power of Secure and Scalable Generative AI : A Deep Dive into AWS and SAP's Cutting-Edge Collaboration

This article explores the groundbreaking collaboration between Amazon Web Services (AWS) and SAP in developing secure and scalable generative AI solutions for businesses. It examines the critical need for robust AI implementations in the face of growing security and scalability challenges, highlighting the unique strengths that AWS and SAP bring to their partnership. The article delves into real-world applications across industries, discusses key benefits for businesses including operational efficiency and enhanced decision-making, and outlines implementation strategies and best practices. Finally, it looks ahead to emerging trends and the long-term vision for AI in business, emphasizing how the AWS-SAP collaboration is poised to shape the future of enterprise AI.

Robust and stochastic sparse subspace clustering

Sparse subspace clustering (SSC) has been widely employed in machine learning and pattern recognition, but it still faces scalability challenges when dealing with large-scale datasets. Recently, stochastic SSC (SSSC) has emerged as an effective solution by leveraging the dropout technique. However, SSSC cannot robustly handle noise, especially non-Gaussian noise, leading to unsatisfactory clustering performance. To address the above issues, we propose a novel robust and stochastic method called stochastic sparse subspace clustering with the Huber function (S3CH). The key idea is to introduce the Huber surrogate to measure the loss of the stochastic self-expression framework, thus S3CH inherits the advantage of the stochastic framework for large-scale problems while mitigating sensitivity to non-Gaussian noise. In algorithms, an efficient proximal alternating minimization (PAM)-based optimization scheme is developed. In theory, the convergence of the generated sequence is rigorously proved. Extensive numerical experiments on synthetic and six real datasets validate the advantages of the proposed method in clustering accuracy, noise robustness, parameter sensitivity, post-hoc analysis, and model stability.

Exploring Novel Strategies to Alleviate Symptoms of β-Globinopathies: Examining the Potential Role of Embryonic ε-globin Induction

β-thalassemia and sickle cell disease are among the most prevalent genetic blood disorders globally. These conditions arise from mutations in the β-globin gene, leading to defective hemoglobin production and resulting in anemia. Current treatments include γ-globin inducers (e.g., Hydroxyurea), blood transfusions, iron chelation therapy, and bone marrow transplantation. Recently approved disease-modifying agents and promising gene therapies offer hope, yet their broad application is constrained by scalability challenges. Traditionally, research and development for β-globinopathies have focused on γ-globin induction. However, the ε-globin variant, which is active during early embryonic development and subsequently silenced prenatally, was once considered non-inducible by postnatal pharmacological means. Recent studies indicate that, akin to γ-globin, enhancing ε-globin expression could compensate for impaired β-globin synthesis, potentially ameliorating the clinical manifestations of β-globinopathies. This review critically examines the viability of ε-globin induction as a therapeutic strategy for β-thalassemia and sickle cell diseases. It also delves into the burgeoning research on the mechanisms governing ε-globin silencing and its pharmacological reactivation. We conclude with a discussion of prospective research directions and drug development initiatives aimed at exploiting ε-globin's therapeutic promise.

A multi-objective optimization decision-making methodology for fostering synergies in the water-energy-food nexus

Current decision-making methods within the water-energy-food nexus (WEFN) encounter challenges in practicality, portability, scalability, and accuracy. Optimization methods, integrating site-specific data, offer promise for achieving desired outcomes and enhancing practical decision implementation within WEFN systems. However, these methods still struggle with solving multi-objective and multi-constraint problems, poor performance, and decision-making difficulties. This study developed an optimization method for WEFN systems, which integrates an evolutionary algorithm, swarm intelligence algorithm, multistage evolutionary algorithm, and post-optimization theories to address these issues. Performance results demonstrated that the proposed algorithm outperformed traditional metaheuristic optimization algorithms. Specifically, it excelled in tackling high-dimensional constrained problems, surpassing the classical NSGA series algorithms with a 1.34-fold improvement in the comprehensive performance metric HV. Subsequently, this algorithm combined with knee point post-optimization theory was applied to a typical arid region known for food and energy production. Compared with the business-as-usual scenario, the optimized schemes enhance agricultural benefits, saving 13.3 billion m3 of water over the entire planning period. Meanwhile, sustainable energy implementation would yield potential carbon emission reduction benefits of around 2.8 billion CNY. In summary, the proposed method would successfully provide a paradigm for the synergetic decision-making of WEFN systems.

Trustworthy fog: A reputation-based consensus method for IoT with blockchain and fog computing

This paper proposes Trustworthy Fog, a novel reputation-based consensus method for Internet of Things (IoT) systems that leverages blockchain and fog computing technologies. By integrating fog computing’s near-end data processing capabilities with blockchain’s immutability and transparency, the proposed method addresses challenges related to latency, device load, and the adaptability of traditional consensus algorithms to resource-constrained environments. A reputation management module evaluates device and node behaviors, facilitating rapid authentication and consensus processes. Distinct reputation calculation schemes for physical devices and fog nodes aim to prevent reputation centralization through periodic resets of reputation values. Based on these values, a lightweight consensus algorithm balances computational capacity and reputation to select leader nodes. Simulations demonstrate the method’s effectiveness in dynamically reflecting device trustworthiness and ensuring fair consensus participation. This research advances IoT blockchain technology, offering a robust solution for the scalability and security challenges inherent in IoT networks.

Highly Photoresponsive Vertically Stacked Silicon Nanowire Photodetector with Biphasic Current Stimulator IC for Retinal Prostheses

This paper presents an integrated approach for a retinal prosthesis that overcomes the scalability challenges and limitations of conventional systems that use external cameras. Silicon nanowires (SiNWs) are utilized as photonic sensors due to their nanoscale dimensions and high surface-to-volume ratio. To enhance these properties and achieve high photoresponsivity, our research team developed a vertically stacked SiNW structure using a fabrication method entirely based on dry etching. The fabricated SiNW photodetector demonstrated excellent electrical and optical characteristics, including linear I–V characteristics that confirmed ohmic contact formation and high photoresponsivity exceeding 105 A/W across the 400–800 nm wavelength range. The SiNW photodetector, following its integration with a switched capacitor stimulator circuit, exhibited a proportional increase in stimulation current in response to higher light intensity and increased SiNW density. In vitro experiments confirmed the efficacy of the integrated system in inducing neural responses from retinal cells, as indicated by an increased number of neural spikes observed at higher light intensities and SiNW densities. This study contributes to sensor technology by demonstrating an approach to integrating nanostructures and electronic components, which enhances control and functionality.

An end-to-end bi-objective approach to deep graph partitioning

Graphs are ubiquitous in real-world applications, such as computation graphs and social networks. Partitioning large graphs into smaller, balanced partitions is often essential, with the bi-objective graph partitioning problem aiming to minimize both the “cut” across partitions and the imbalance in partition sizes. However, existing heuristic methods face scalability challenges or overlook partition balance, leading to suboptimal results. Recent deep learning approaches, while promising, typically focus only on node-level features and lack a truly end-to-end framework, resulting in limited performance. In this paper, we introduce a novel method based on graph neural networks (GNNs) that leverages multilevel graph features and addresses the problem end-to-end through a bi-objective formulation. Our approach explores node-, local-, and global-level features, and introduces a well-bounded bi-objective function that minimizes the cut while ensuring partition-wise balance across all partitions. Additionally, we propose a GNN-based deep model incorporating a Hardmax operator, allowing the model to optimize partitions in a fully end-to-end manner. Experimental results on 12 datasets across various applications and scales demonstrate that our method significantly improves both partitioning quality and scalability compared to existing bi-objective and deep graph partitioning baselines.

Scalability challenges of machine learning models for estimating walking and cycling volumes in large networks

This study explores the scalability of machine learning models for estimating walking and cycling volumes across the extensive New South Wales (NSW) Six Cities Region in Australia using mobile phone and crowdsourced data. Previous research has focused on localized applications, missing the complexities of larger networks. The research addresses this gap by identifying unique challenges such as the scarcity and representativeness of observed count data, gaps in the crowdsourced and mobile phone data, and inconsistencies in link-level volume estimates. We propose and demonstrate the application of strategies like enhancing geographical diversity of observed count data and employing an extensive cross-validation approach in model training and testing. By leveraging various auxiliary datasets, the study demonstrates the effectiveness of these strategies in improving model performance. These findings provide valuable insights for transportation modelers, policymakers, and urban planners, offering a robust framework for supporting sustainable transportation infrastructure and policies with advanced data-driven methodologies.

Harnessing blockchain technology in banking to enhance financial inclusion, security, and transaction efficiency

This paper examines the potential of blockchain technology in revolutionizing the banking sector by enhancing financial inclusion, security, and transaction efficiency. With traditional banking systems often excluding underbanked populations and being prone to security vulnerabilities, blockchain offers a decentralized and secure alternative. The study explores how blockchain can lower barriers to financial services for underserved communities, reduce fraud, and streamline transaction processes. The research methodology includes a comprehensive review of existing literature, case studies of early blockchain adopters in banking, and an analysis of the technology's scalability and integration challenges. Key findings indicate that blockchain can significantly improve financial inclusion by providing accessible banking solutions, especially in developing regions. Additionally, its cryptographic security features are shown to mitigate risks associated with cyberattacks and fraud, while smart contracts and peer-to-peer transactions reduce operational costs and processing times. However, the paper also highlights potential regulatory hurdles and the need for robust governance frameworks to ensure widespread adoption. The study concludes with strategic recommendations for banks and financial institutions on implementing blockchain technology, emphasizing the importance of collaboration with regulators and the continuous evolution of blockchain infrastructure. Future prospects include the integration of blockchain with emerging technologies such as artificial intelligence and the Internet of Things (IoT), which could further enhance the capabilities of financial institutions. This paper underscores the transformative impact of blockchain on banking, offering insights into strategies that organizations can adopt to stay competitive and inclusive in a rapidly changing financial landscape.

Integrating Quantum Computing Into Blockchain: Strategies for Overcoming Scalability and Security Challenges

, , , , & Department of Computer Science and Pure & Applied Physics Ladoke Akintola University of Technology Ogbomoso, Oyo State, Nigeria E-mails: golamiji@lautech.edu.ng, soolabiyisi@lautech.edu.ng, woismaila@lautech.edu.ng, ofalade28@pgschool.lautech.edu.ng, ljmkadeola@gmail.com ABSTRACT The rapid progression of quantum computing has created new opportunities for advancing blockchain technology, particularly in addressing its two primary challenges which are scalability and security. As blockchain adoption grows, traditional consensus mechanisms reveal significant inefficiencies, causing transaction bottlenecks and increased latency. Additionally, the advent of quantum computing poses a significant threat to the classical cryptographic algorithms foundational to blockchain security. This research introduces an innovative approach that harnesses quantum computing to address these dual challenges. To enhance scalability, this research proposes integrating Grover's algorithm and Post Quantum encryption alogrithms to optimize transaction selection and validation, significantly reducing time complexity compared to classical methods. On the security front, the research introduces post-quantum cryptographic techniques to strengthen blockchain networks against quantum adversaries. Simulations conducted on IBM Qiskit Quantum Computer Simulator demonstrate that the proposed framework achieves superior efficiency, cost-effectiveness, and faster transaction processing with Grover's algorithm and Post Quantum encryption alogrithms compared to the current classical systems. These improvements highlight the potential benefits of utilizing quantum computing to deploy quantum-enhanced blockchain. Keywords: Quantum, Computing, IBM, Qiskit, blockchain, Post-Quantum, Encryption CISDI Journal Reference Format Gabriel, O., Olabiyisi, S.O., Ismaila, W.O., Falade, O.A., & Alawode O. (2024): Integrating Quantum Computing Into Blockchain: Strategies for Overcoming Scalability and Security Challenges. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 3, Pp 15-28. dx.doi.org/10.22624/AIMS/CISDI/V15N3P2 Available online at www.isteams.net/cisdijournal

DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING

This paper comprehensively analyzes distributed high-performance computing methods for accelerating deep learning training. We explore the evolution of distributed computing architectures, including data parallelism, model parallelism, and pipeline parallelism, and their hybrid implementations. The study delves into optimization techniques crucial for large-scale training, such as distributed optimization algorithms, gradient compression, and adaptive learning rate methods. We investigate communication-efficient algorithms, including Ring All Reduce variants and decentralized training approaches, which address the scalability challenges in distributed systems. The research examines hardware acceleration and specialized systems, focusing on GPU clusters, custom AI accelerators, high-performance interconnects, and distributed storage systems optimized for deep learning workloads. Finally, we discuss this field's challenges and future directions, including scalability-efficiency trade-offs, fault tolerance, energy efficiency in large-scale training, and emerging trends like federated learning and neuromorphic computing. Our findings highlight the synergy between advanced algorithms, specialized hardware, and optimized system designs in pushing the boundaries of large-scale deep learning, paving the way for future breakthroughs in artificial intelligence.

WIRELESS CHARGING TECHNOLOGY FOR ELECTRIC VEHICLES: CURRENT TRENDS AND ENGINEERING CHALLENGES

Wireless charging technology (WCT) for electric vehicles (EVs) has gained significant attention as a promising alternative to traditional plug-in charging systems due to its convenience and efficiency. This paper systematically reviews 100 peer-reviewed studies on the advancements in WCT, focusing on wireless power transfer (WPT) methods such as inductive and resonant coupling, and the critical engineering challenges that limit widespread adoption. Key issues identified include power transfer efficiency, misalignment between the vehicle and charging pad, and electromagnetic interference (EMI). Additionally, this review explores the infrastructure and scalability challenges of implementing WCT in urban environments and highways, including the potential of dynamic wireless charging systems, which allow EVs to charge while in motion. Despite recent innovations, such as adaptive control systems and advanced coil designs, gaps remain in the research on long-term feasibility and standardization. This study emphasizes the need for interdisciplinary collaboration across technical, economic, and policy domains to support the large-scale commercialization of WCT.

Titania-Based Coral-Structured Solar Absorber Coating with Improved Scalability and Durability at High Temperature.

Solar energy harvesting and storage are essential in the future mix of renewable energy technologies. Hierarchical coral-structured coatings have been shown to yield high solar absorptance in concentrating solar thermal (CST) systems. However, interfacial delamination and scalability challenges owing to material complexity pose significant hurdles for the widespread industrial adoption of these hierarchical CST coatings. Here, a coral-structured coating is proposed whose black pigments are strongly bonded by titania, which is a material that mitigates interfacial delamination. Importantly, this coating follows a facile deposition procedure suitable for large-scale solar receivers. The drone-deposited coating inhibits cation diffusion and maintains a stable solar absorptance of even after long-term (3000 h) high-temperature ( ) aging. The scalability of developed coating represents a substantial advancement in the implementation of light-trapping enhancement and maintenance approaches across a wide range of CSTapplications.

Mechanistic Insights and Emerging Trends in Photocatalytic Dye Degradation for Wastewater Treatment

AbstractThis review explores advancements, challenges, and considerations in photocatalytic dye degradation for sustainable wastewater treatment. It highlights smart photocatalyst design, visible‐light‐responsive materials, and co‐catalyst engineering, which enhance system efficacy. Despite environmental concerns, the eco‐friendly aspects of photocatalysis offer a promising alternative to traditional methods. Future perspectives emphasize nanotechnology's role in developing effective photocatalysts and integrating visible‐light and solar‐driven systems to meet sustainability goals. Efforts in co‐catalyst engineering and reactor design aim to optimize processes, addressing kinetic and scalability challenges, while economic research focuses on reducing costs to improve competitiveness.

Effect of Size Tuning of Hexagonal SnS2 Nanosheet on the Efficiency of Photocatalytic Degradation Processes.

Recent research on SnS2 materials aims to enhance their photocatalytic efficiency for water pollution remediation through doping and constructing heterojunctions. These methods face challenges in cost-effectiveness and practical scalability. This study synthesizes hexagonal SnS2 nanosheets of various sizes via a hydrothermal method, assessing their performance in degrading methyl orange (MO) and reducing hexavalent chromium (Cr(VI)). The results show that smaller SnS2 nanosheets exhibit higher photocatalytic efficiency under sunlight. Specifically, 50mg of small-sized nanosheets degraded 100ml of MO (10 mgL-1) in 30min and reduced Cr(VI) (10 mgL-1) in 105min. The enhanced performance is attributed to: i) an energy bandgap of 2.17eV suitable for visible light, and ii) more surface sulfur (S) vacancies in smaller nanosheets, which create electronic states near the Fermi level, reducing electron-hole recombination. This study offers a straightforward strategy for improving 2D materials like SnS2.

Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain.