Abrar Hossain

Abrar Hossain, Kishwar Ahmed

Modern High Performance Computing (HPC) centers face growing challenges in ingesting large and diverse data streams. These issues often create bottlenecks that limit bandwidth utilization and delay scientific progress. Traditional static allocation and simple queuing methods are often insufficient. This paper presents a dynamic, value-based approach to bandwidth allocation. We formalize the problem by incorporating both network and processing constraints. To address it, we introduce two auction-based mechanisms: the Greedy Value Density Auction, which is computationally efficient, and the Vickrey--Clarke--Groves (VCG) Knapsack Auction, which provides strong theoretical guarantees. Both mechanisms rely on user bids that specify data requirements and scientific value. The objective is to maximize the total value of successful transfers, commonly referred to as social welfare. Simulation results demonstrate that the proposed mechanisms significantly outperform First Come First Served (FCFS) baselines. Under high-load conditions, they reduce average and tail completion delays by more than 80%. Predictability also improves, with the coefficient of variation of delay decreasing by 75--85%. Network stability increases as well, with load volatility, measured by the peak-to-average ratio, decreasing by 60--70%. These results indicate that value-driven, adaptive bandwidth allocation can reduce congestion, improve resource utilization, and provide fairer access based on scientific importance.

Tasnimul Hasan, Abrar Hossain, Mufakir Qamar Ansari et al.

The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial systems, creating substantial opportunities for growth. However, this growth has also heightened the risk of cyberattacks, necessitating robust security measures to protect IIoT networks. Intrusion Detection Systems (IDS) are essential for identifying and preventing abnormal network behaviors and malicious activities. Despite the potential of Machine Learning (ML)--based IDS solutions, existing models often face challenges with class imbalance and multiclass IIoT datasets, resulting in reduced detection accuracy. This research directly addresses these challenges by implementing six innovative approaches to enhance IDS performance, including leveraging an autoencoder for dimensional reduction, which improves feature learning and overall detection accuracy. Our proposed Decision Tree model achieved an exceptional F1 score and accuracy of 99.94% on the Edge-IIoTset dataset. Furthermore, we prioritized lightweight model design, ensuring deployability on resource-constrained edge devices. Notably, we are the first to deploy our model on a Jetson Nano, achieving inference times of 0.185 ms for binary classification and 0.187 ms for multiclass classification. These results highlight the novelty and robustness of our approach, offering a practical and efficient solution to the challenges posed by imbalanced and multiclass IIoT datasets, thereby enhancing the detection and prevention of network intrusions.

Abrar Hossain, Abdel-Hameed A. Badawy, Mohammad A. Islam et al.

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices.