Publications

2023

1. 

   GPU Accelerated Dataflow Analysis

   Akram Aziz, Basant Abdelaal, Youssef Hussein, and 5 more authors

   Jun 2023

   Unpublished Manuscript

   Abs PDF

   Dataflow analysis is a critical technique for detecting software bugs during development. However, existing tools often suffer from scalability and speed issues. In this work, we explore the use of GPUs to accelerate dataflow analysis by targeting dataflow analysis problems. We propose a GPU-compatible implementation based on the matrix representation approach. By representing instructions as matrices, we achieve a significant speedup of up to 250𝑋 compared to CPU implementations. Our research aims to accelerate dataflow analysis using GPUs, providing a more efficient solution for real- time development environments. This approach overcomes the speed limitations of existing dataflow analysis tools, enabling faster bug detection and more precise analysis. By leveraging the parallel processing capabilities of GPUs, we handle large-scale programs and complex dataflow effectively. Our work bridges the gap between the increasing complexity of software systems and the need for scalable and efficient bug detection mechanisms. By accelerating dataflow analysis using GPUs, we enhance the precision and speed of static analysis, enabling faster bug detection and reporting in software development.

2. 

   Economic Land Utilization Optimization Model

   Ossama A. Hosny, Elkhayam M. Dorra, Youssef Hussein, and 8 more authors

   Sustainability, Jun 2023

   Abs PDF

   Recently, population growth and resource depletion have been matched by a growing demand for self-sustaining communities. Numerous studies promote sustainable solutions to the concerns of climate change and food scarcity. This study aims at creating an automated Economic Land Utilization Optimization Model (ELUOM) that identifies sustainable and cost-effective agricultural practices. Soil, water & climatic characteristics of over 400 crops are gathered in a relational database to build the model. Evolutionary algorithms are utilized to filter the database based on user input. Optimization process is then performed on all possible utilization plans of the filtered crops to maximize the 20-year return while minimizing water consumption. The model is verified on a case study in Giza, Egypt where it shows the potential of increasing the return/m3 of water by 370% versus current practices. This research also studies the application of ELOUM on a vacant plot in the American university in Cairo, Egypt.

2022

1. 

   Designing an Automated Multi-Objective Optimization Model for Integrated and Sustainable Farming

   Ossama A. Hosny, Elkhayam M. Dorra, Youssef Hussein, and 8 more authors

   Mar 2022

   Abs PDF

   The challenges of climate change, water and food scarcity have created the need for planning tools that assist in the agricultural decision-making process. This study aims to propose a multi-objective automated optimization model that uses an embedded comprehensive database to maximize the economic return whilst ensuring minimal water consumption. The suggested model capitalizes on big data in farming and greenhouses to filter all viable scenarios for a given soil, climate properties, and water availability. Evolutionary and genetic algorithms are employed to assess the long-term production, profitability, and water consumption of the scenarios. This study describes how the automated model operates through applying it to a case study to optimize the use of a land plot in Giza, Egypt, for farming purposes. The research opens the door for further application of the proposed model in different contexts both regionally and internationally, thus playing a vital role in the water and food nexus.

2021

1. 

   Dual-criticality scheduling on non-preemptive, dynamic processors using RL agents

   Nourhan Sakr, Youssef Hussein, and Karim Farid

   Jul 2021

   Abs PDF

   Real-time embedded systems have stringent non-functional requirements on cost, weight, and energy that give rise to the study of mixed-criticality (mc) systems, where functionalities of di€erent criticality levels are consolidated into a shared hardware platform (Barhorst et al. [1], Burns and Davis [6]). The literature discusses the schedulability of mc systems under various conditions and objectives (Baruah et al. [3], Gu et al. [7], Baruah et al. [4]). In this work, we study a dual-criticality, non-preemptive system with a varying-speed uniprocessor. This varying-speed processor makes mc scheduling dynamic: In real-time systems, it cannot be predetermined if, when or for how long the processor would degrade, i.e. its speed would drop. Under speed stochasticity, it is critical to guarantee the running of high (hi) criticality jobs by their deadlines, sometimes even at the cost of not running low (lo) criticality jobs at all, when operating under degradation. Scheduling mc systems non-preemptively (even without degradation) is N P-hard (Lenstra et al. [9]). Baruah and Guo [5] model an LP to preemptively schedule dualcriticality jobs on a varying-speed processor. Agarwal and Baruah [2] further discuss the online nature of the problem and its intractability. We agree with the authors that mc scheduling is inherently an online problem, as it better depicts real-time scheduling and the dynamic nature of this problem. Therefore, we devise deep reinforcement learning (deep rl or drl) to tackle the problem presented by Baruah and Guo [5], under both the o„ine and online setting.