Engineering Technology Works

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https://hdl.handle.net/1805/7625

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Browsing Engineering Technology Works by Author "Abdallah, Mustafa"

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    DAG-based Task Orchestration for Edge Computing
    (IEEE, 2022) Li, Xiang; Abdallah, Mustafa; Suryavansh, Shikhar; Chiang, Mung; Bagchi, Saurabh; Engineering Technology, Purdue School of Engineering and Technology
    Edge computing promises to exploit underlying computation resources closer to users to help run latency-sensitive applications such as augmented reality and video analytics. However, one key missing piece has been how to incorporate personally owned, unmanaged devices into a usable edge computing system. The primary challenges arise due to the heterogeneity, lack of interference management, and unpredictable availability of such devices. In this paper we propose an orchestration framework IBDASH, which orchestrates application tasks on an edge system that comprises a mix of commercial and personal edge devices. IBDASH targets reducing both end-to-end latency of execution and probability of failure for applications that have dependency among tasks, captured by directed acyclic graphs (DAGs). IBDASH takes memory constraints of each edge device and network bandwidth into consideration. To assess the effectiveness of IBDASH, we run real application tasks on real edge devices with widely varying capabilities. We feed these measurements into a simulator that runs IBDASH at scale. Compared to three state-of-the-art edge orchestration schemes and two intuitive baselines, IBDASH reduces the end-to-end latency and probability of failure, by 14% and 41% on average respectively. The main takeaway from our work is that it is feasible to combine personal and commercial devices into a usable edge computing platform, one that delivers low and predictable latency and high availability.
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    Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems
    (Association for Computing Machinery, 2021) Abdallah, Mustafa; Woods, Daniel; Naghizadeh, Parinaz; Khalil, Issa; Cason, Timothy; Sundaram, Shreyas; Bagchi, Saurabh; Engineering Technology, Purdue School of Engineering and Technology
    We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making. We provide empirical evidence for the existence of such behavioral bias model through a controlled subject study with 145 participants. We then propose three learning techniques for enhancing decision-making in multi-round setups. We illustrate the benefits of our decision-making model through multiple interdependent real-world systems and quantify the level of gain compared to the case in which the defenders are behavioral. We also show the benefit of our learning techniques against different attack models. We identify the effects of different system parameters (e.g., the defenders' security budget availability and distribution, the degree of interdependency among defenders, and collaborative defense strategies) on the degree of suboptimality of security outcomes due to behavioral decision-making.