arXiv subjects

Abrar Hossain

Publications and source records attributed to Abrar Hossain.

7 recordsLinked to original sources

arXiv2026

XOR Bidding and Knapsack Formulations for HPC Network Resource Allocation

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.

cs.NI↗

arXiv2025

From Text to Returns: Using Large Language Models for Mutual Fund Portfolio Optimization and Risk-Adjusted Allocation

Generative AI (GenAI) has enormous potential for improving two critical areas in investing, namely portfolio optimization (choosing the best combination of assets) and risk management (protecting those investments). Our study works at this intersection, using Large Language Models (LLMs) to upgrade how financial decisions are traditionally made. This research specifically tested how well advanced LLMs like Microsoft Phi 2, Mistral 7B, and Zypher 7B can create practical, risk-aware strategies for investing mutual funds in different sectors of the economy. Our method is sophisticated: it combines a Retrieval-Augmented Generation (RAG) pipeline, which enables the LLM to check external, real-time data with standard financial optimization methods. The model's advice is context-aware because we feed it large economic signals, like changes in the global economy. The Zypher 7B model was the clear winner. It consistently produced strategies that maximized investment returns while delivering better risk-adjusted results than the other models. Its ability to process complex relationships and contextual information makes it a highly powerful tool for financial allocation. In conclusion, our findings show that GenAI substantially improves performance over basic allocation methods. By connecting GenAI to real-world financial applications, this work lays the groundwork for creating smarter, more efficient, and more adaptable solutions for asset management professionals.

cs.CE↗

arXiv2025

Power-Aware Scheduling for Multi-Center HPC Electricity Cost Optimization

This paper introduces TARDIS (Temporal Allocation for Resource Distribution using Intelligent Scheduling), a novel power-aware job scheduler for High-Performance Computing (HPC) systems that minimizes electricity costs through both temporal and spatial optimization. Our approach addresses the growing concerns of energy consumption in HPC centers, where electricity expenses constitute a substantial portion of operational costs and have a significant financial impact. TARDIS employs a Graph Neural Network (GNN) to accurately predict individual job power consumption, then uses these predictions to strategically schedule jobs across multiple HPC facilities based on time-varying electricity prices. The system integrates both temporal scheduling, shifting power-intensive workloads to off-peak hours, and spatial scheduling, distributing jobs across geographically dispersed centers with different pricing schemes. We evaluate TARDIS using trace-based simulations from real HPC workloads, demonstrating cost reductions of up to 18% in temporal optimization scenarios and 10 to 20% in multi-site environments compared to state-of-the-art scheduling approaches, while maintaining comparable system performance and job throughput. Our comprehensive evaluation shows that TARDIS effectively addresses limitations in existing power-aware scheduling approaches by combining accurate power prediction with holistic spatial-temporal optimization, providing a scalable solution for sustainable and cost-efficient HPC operations.

cs.DC↗

arXiv2025

Scalable HPC Job Scheduling and Resource Management in SST

Efficient job scheduling and resource management contribute towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a cycle-accurate and parallel discrete-event simulator. Our proposed simulator includes state-of-the-art job scheduling algorithms and resource management techniques. Additionally, it introduces workflow management components that support the simulation of task dependencies and resource allocations, crucial for workflows typical in scientific computing and data-intensive applications. We present the validation and scalability results of our job scheduling simulator. Simulation shows that our simulator achieves good accuracy in various metrics (e.g., job wait times, number of nodes usage) and also achieves good parallel performance.

cs.DC↗

arXiv2025

Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning

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.

cs.LG↗

arXiv2025

HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach

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.

cs.PF↗

arXiv2021

Software startup education: gamifying growth hacking

Startups seek to create highly scalable business models. For startups, growth is thus vital. Growth hacking is a marketing strategy advocated by various startup practitioner experts. It focuses on using low cost practices while utilizing existing platforms in creative ways to gain more users for the service. Though topics related to growth hacking such as marketing on a general level have been extensively studied in the past, growth hacking as a practitioner-born topic has not seen much interesting among the academia. To both spark interest in growth hacking, and to facilitate teaching growth hacking in the academia, we present two board games intended to serve as an engaging introduction to growth hacking for students.

cs.CY↗