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Detection and Localization of Channel-Short Faults in Regular On-Chip Interconnection Networks

SN computer science 2023年第5期4卷 1-18页

作者： Bhowmik, Biswajit Swami Vivekananda NoC Lab BRICS Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal 575025 India

With the rapid developments in VLSI technology, the communication channels in networks-on-chip (NoCs) can place many wires for sustaining high-performance requirements over the communication bottleneck in multicore, multiprocessor systems-on-chip (MPSoCs). Consequently, NoC channels, due to increased wire density, are exposed to different logic level faults, e.g., short resulting in reliability and yield issues in NoC-based systems. These faults can appear at any stage of the lifetime of a chip. While existing in an NoC communication architecture, the channel-short faults bring the system into various failures that surprisingly cause a significant deviation from its expected performance. In this work, an online, distributed test solution is presented that detects and diagnoses intra-channel and inter-channel short faults and analyzes the effect of these faults on various performance metrics. Fault simulations ensure up to 100% coverage metrics. Network simulation shows insight into the impact of channel shorts in NoC performances. It is observed that the amount of test time is reduced to 10 × concerning a set of prior works while employed for a group of NoCs. It is also seen on these NoCs that average packet latency is improved by 15.14–46.79% while energy consumption is reduced by 13.68–39.13% by the current solution than the set of existing solutions. Moreover, the proposed solution scales to all NoCs irrespective of size, topology, and channel width at an acceptable test cost. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Defect-based testing Diagonal node selection Fault modelling and evaluation Intra-channel and inter-channel manufacturing shorts Model scalability Networks on a chip Performance analysis and measurement System level failures

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Optimizing Feature Selection in Big Data: A Hybrid Spark and Fuzzy Approach

Optimizing Feature Selection in Big Data: A Hybrid Spark and...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Hada, Aman Singh Sahoo, Gyanaballav Samir Vamsi, Chinnapareddy Krishna Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Department of Computer Science and Engineering Karnataka Mangalore575025 India

ISBN: (纸本)9798331517892

The exponential growth of big data presents both immense opportunities and significant challenges. While vast datasets hold the key to unlocking groundbreaking insights, efficiently extracting value requires sophisticated feature selection techniques. Traditional methods often struggle with the sheer volume and complexity of big data. This paper addresses this challenge by proposing a novel hybrid feature selection algorithm by leveraging Apache PySpark's distributed computing power. Combining a robust feature selection technique with a novel weighting scheme, our method outperforms existing hypercuboid and fuzzy Rough Set methods. The hybrid approach achieves superior accuracy of 72.1% with a reduced feature set, demonstrating its effectiveness in identifying salient features for big data analysis. © 2024 IEEE.

关键词： Data accuracy

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Emergence, Evolution, and Applications of Medical Cyber-Physical Systems

Emergence, Evolution, and Applications of Medical Cyber-Phys...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Rathore, Rohan Bhowmik, Biswajit Maharshi Patanjali CPS Lab BRICS Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Bharat Mangalore India

ISBN: (纸本)9789819746569

The speedy advancement of technology has given rise to a new era of engineered systems called cyber-physical systems (CPS), which are redefining lifestyles worldwide through computer, networking, and control technologies. From health care to transportation, CPS has transformed several industries. By bringing about significant improvements in patient care, diagnosis, and treatment, Medical Cyber-Physical Systems (MCPS) transform the healthcare industry. Exploration of the area is, therefore, imperative. The paper discusses the trajectory of MPCS. It covers the development, necessity, and significance of MCPS. Next, we examine the various uses of MCPS in contemporary life. We then look at many issues and current developments in the medical organization. It makes it easier for patients, medical professionals, and equipment to share information effectively, which allows for prompt decision-making and preventative measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Remote patient monitoring

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Ways of Balancing Load in Microservice Architecture

Ways of Balancing Load in Microservice Architecture

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Saxena, Divarshana Bhowmik, Biswajit Swami Vivekananda NoC Lab BRICS Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore575025 India

ISBN: (纸本)9789819746569

Microservices architecture has emerged as a modern paradigm to overcome challenges associated with monolithic architecture, such as scalability, deployment, and flexibility. Microservice architecture is a relatively new approach in comparison with other paradigms. It has immense potential to enhance deployment, design, container orchestration, and expansion across different computing environments, such as cloud and edge. One of the most essential features of microservice architecture is its ability to handle scaling and load balancing. The load balancer works with a scaler to distribute load efficiently across multiple instances. This paper explains the basics of load balancing, including static and dynamic algorithms, their applications, and limitations. It emphasizes the crucial role of load balancing in popular microservices orchestrators like Kubernetes, Docker Swarm, and Spring Cloud. Furthermore, we examined the existing state of the art and identified limitations associated with load balancing in microservice architecture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Scalability

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Money Laundering Detection in Imbalanced E-wallet Transactions with Threshold Optimization 9

Money Laundering Detection in Imbalanced E-wallet Transactio...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Doddamani, Sachin Shivaji Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science And Engineering Mangalore Surathkal575025 India

ISBN: (纸本)9798350394474

The swift embrace of technology within the financial industry has driven the extensive utilization of electronic payment systems, providing smooth money transfers and substituting outdated paper-based procedures. Consequently, mobile payment systems centered on electronic wallets (e-wallets) have signifi-cantly transformed contemporary finance, introducing improved security measures and transaction functionalities. However, the prevalence of unlawful activities, including money laundering and associated fraud via e-wallets, presents a substantial threat to the integrity of the financial sector. This research paper delves into the pivotal role of machine learning models in identifying money laundering activities within e-wallet transactions. The study focuses on addressing the imbalance inherent in the PaySim dataset through the oversampling technique. Employing three distinct models - Logistic Regression (LR), Gradient Boosting, and XGBoost - the research systematically evaluates their effectiveness. Notably, XGBoost emerged as the standout performer, showcasing exceptional accuracy at 99.88%, precision at 0.9984, and sensitivity at 0.999. Furthermore, a threshold moving technique is employed to enhance the model's efficiency, optimizing its performance in detecting potential instances of money laundering within e-wallet transactions. © 2024 IEEE.

关键词： Finance

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Enhancing MPI Communication Efficiency for Grid-Based Stencil Computations

Enhancing MPI Communication Efficiency for Grid-Based Stenci...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Goudar, Santosh Iranagouda Nayaka, Premkumar Sj Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Lab Department of Computer Science and Engineering SurathkalBharat Mangalore India

ISBN: (纸本)9798331517892

In parallel computing, where efficiency and speed are crucial, the Message Passing Interface (MPI) is a fundamental paradigm for managing large-scale distributed memory systems. MPI is critical to complex computational tasks, particularly in grid-based computations that solve intricate numerical problems by discretizing spatial domains into structured grids. However, MPI Cartesian communicators exhibit limitations in handling these computations effectively, especially when managing large-scale data exchanges and complex stencil patterns. This paper addresses these challenges by presenting an integrated approach that combines MPI collective and Cartesian communication methods. The proposed solution simplifies data distribution, eliminates redundant interfaces, and enhances communication efficiency. Experimental results show a 43% reduction in execution time and a 40% decrease in communication overhead, with scalability improvements achieving 12.5x speedup using 64 processes. These quantitative outcomes demonstrate the advan-tages of the proposed method over conventional MPI Cartesian approaches, establishing it as a reliable framework for advancing High-Performance Computing (HPC) capabilities in grid-based applications. © 2024 IEEE.

关键词： Message passing

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AutoCov22: A Customized Deep Learning Framework for COVID-19 Detection

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SN computer science 2023年第5期4卷 659页

作者： Bhowmik, Biswajit Varna, Shrinidhi Kumar, Adarsh Kumar, Rahul Ishwarchandra Vidyasagar AIT Lab BRICS Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal 575025 India

The novel coronavirus disease 2019 (COVID-19) spill has spread rapidly and appeared as a pandemic affecting global public health. Due to the severe challenges faced with the increase of suspected cases, more testing methods are explored. These methods, however, have several disadvantages, such as test complexity and associated problems—sensitivity, reproducibility, and specificity. Hence, many of them need help to achieve satisfactory performance. Motivated by these shortcomings, this work proposes a custom deep neural network framework named “AutoCov22” that detects COVID-19 by exploiting medical images—chest X-ray and CT-scan. First, multiple neural networks extract deep features from the input medical images, including popularly used VGG16, ResNet50, DenseNet121, and InceptionResNetV2. Then, the extracted features are fed to different machine-learning techniques to identify COVID-19 cases. One objective of this work is to quicken COVID-19 detection. Another goal is to reduce the number of falsely detected cases by a significant margin. Comprehensive simulation results achieve a classification accuracy of 99.74%, a precision of 99.69%, and a recall of 98.80% on exercising chest X-ray images. Extended experiment results in accuracy, precision, and recall up to 87.18%, 84.98%, and 85.66%, respectively, in processing CT-scan images. Thus, the AutoCov22 approach demonstrates a promising and plausible best solution over several methods in the state-of-the-art. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 CT-scan images Deep neural networks False prediction reduction Test accuracy Transfer learning techniques X-ray images

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Analysis of Selected Load Balancing Algorithms in Containerized Cloud Environment for Microservices 4

Analysis of Selected Load Balancing Algorithms in Containeri...

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4th IEEE International Conference on VLSI Systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Saxena, Divarshana Bhowmik, Biswajit National Institute Of Technology Karnataka Swami Vivekananda NoC Lab Brics Lab Department Of Computer Science And Engineering Mangalore Surathkal575025 India

ISBN: (纸本)9798350362268

Microservice architecture has become a widely accepted solution to address the challenges, particularly scala-bility, deployment, and flexibility associated with monolithic architecture. A vital attribute of the microservices architecture is its capability to handle load balancing on a large scale. The load balancer collaborates with a scaler to distribute the workload efficiently across multiple instances. In the literature, different studies employ load-balancing algorithms for efficient microservice load balancing. These works overlook cloud-based microservice applications or focus solely on virtual machines, neglecting containers. This paper addresses these limitations by comparatively assessing selected load-balancing algorithms. The three most used algorithms, random, round-robin, and least connection, are studied on a microservice application. The extensive experiments are conducted using Elastic Container Service (ECS) of Amazon Web Service (AWS) for containerized cloud setup where each service resides in a cluster and traffic is generated through Locust. Experimental results show that throughput and response time range of 6.2-288.7 and 312.2-3375.8 ms, respectively. © 2024 IEEE.

关键词： Containers

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Leveraging Hybrid Modeling for Enhanced Runtime Prediction in Big Data Jobs

Leveraging Hybrid Modeling for Enhanced Runtime Prediction i...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Singh, Rahul Zadokar, Vivek Narayan Kumar, Sunil Doddamani, Sachin Shivaji Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798331517892

In an era of rapid data expansion, big data has significantly transformed various industries, redefining the processes of data processing, analysis, and utilization. The widespread adoption of digital technologies has driven this surge in big data, leading to an unprecedented accumulation of information from sources such as social media, sensors, and transactions. As big data evolves, it presents significant challenges and unique opportunities, necessitating innovative solutions to leverage its potential fully. One critical challenge in big data environments is accurately predicting job runtimes, essential for optimizing resource utilization and enhancing overall system performance. Current approaches, including analytical models and machine learning algorithms, often need help to manage the complexities of unstructured data and maintain interpretability effectively. This paper proposes a novel hybrid modeling approach that integrates the strengths of both techniques to improve job runtime predictions. The hybrid architecture combines an analytical model, which captures the intricate characteristics of jobs and execution environments, with a machine learning model trained to detect patterns and relationships in historical data. As demonstrated on real-world big datasets, the hybrid model achieves greater accuracy by merging these capabilities. Utilizing the flexible capabilities of PySpark and incorporating advanced feature engineering techniques, the model dynamically adapts to various dataset sizes and complexities, ensuring robust performance across different scenarios. © 2024 IEEE.

关键词： Prediction models

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Dynamic Checkpointing: Fault Tolerance in High-Performance Computing 3

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhowmik, Biswajit Verma, Tushar Dineshbhai, Navdeep Dadhania Reddy, Manideep Raya Vsrs Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad Qc Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528201

Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 IEEE.

关键词： Parallel processing systems

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