Browsing by Author "Elbery, Ahmed"

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    City-Wide Eco-Routing Navigation Considering Vehicular Communication Impacts
    Elbery, Ahmed; Rakha, Hesham A. (MDPI, 2019-01-12)
    Intelligent Transportation Systems (ITSs) utilize Vehicular Ad-hoc Networks (VANETs) to collect, disseminate, and share data with the Traffic Management Center (TMC) and different actuators. Consequently, packet drop and delay in VANETs can significantly impact ITS performance. Feedback-based eco-routing (FB-ECO) is a promising ITS technology, which is expected to reduce vehicle fuel/energy consumption and pollutant emissions by routing drivers through the most environmentally friendly routes. To compute these routes, the FB-ECO utilizes VANET communication to update link costs in real-time, based on the experiences of other vehicles in the system. In this paper, we study the impact of vehicular communication on FB-ECO navigation performance in a large-scale real network with realistic calibrated traffic demand data. We conduct this study at different market penetration rates and different congestion levels. We start by conducting a sensitivity analysis of the market penetration rate on the FB-ECO system performance, and its network-wide impacts considering ideal communication. Subsequently, we study the impact of the communication network on system performance for different market penetration levels, considering the communication system. The results demonstrate that, for market penetration levels less than 30%, the eco-routing system performs adequately in both the ideal and realistic communication scenarios. It also shows that, for realistic communication, increasing the market penetration rate results in a network-wide degradation of the system performance.
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    Classification of Arabic Documents
    Elbery, Ahmed (2012-12-19)
    Arabic language is a very rich language with complex morphology, so it has a very different and difficult structure than other languages. So it is important to build an Arabic Text Classifier (ATC) to deal with this complex language. The importance of text or document classification comes from its wide variety of application domains such as text indexing, document sorting, text filtering, and Web page categorization. Due to the immense amount of Arabic documents as well as the number of internet Arabic language users, this project aims to implement an Arabic Text-Documents Classifier (ATC).
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    IDEAL Pages
    Farghally, Mohammed; Elbery, Ahmed (2014-05-10)
    The main goal of this project is to provide a convenient Web enabled interface to a large collection of event-related webpages supporting the two main services of browsing and searching. We first studied the events and decided what fields are required to build the events index based on the dataset available to us. We then configured a SolrCloud with a collection based on these fields in the Schema.xml file. Then we built a Hadoop Map-Reduce function along with SolrCloud to index documents related to the data about 60 events crawled from the Web. Then we were able to find a way to interface with the Solr server and indexed documents through a PHP server application. Finally, we were able to design a convenient user interface that allows users to browse the documents by event category and event name as well as to search the document collection for particular keywords.
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    Toward Fair and Efficient Congestion Control: Machine Learning Aided Congestion Control (MLACC)
    Elbery, Ahmed; Lian, Yi; Li, Geng (ACM, 2023-06-29)
    Emerging inter-datacenter applications require massive loads of data transfer which makes them sensitive to packet drops, high latency, and fair resource sharing. However, current congestion control (CC) protocols do not guarantee the optimal outcome of these metrics. In this paper, we introduce a new CC technique, Machine Learning Aided Congestion Control (MLACC), that combines heuristics and machine learning (ML) to improve these three network metrics. The proposed technique achieves a high level of fairness, minimum latency, and minimum drop rate. ML is utilized to estimate the ratio of the available bandwidth of the bottleneck link while the heuristic uses this ratio to enable end-points to cooperatively limit the shared bottleneck link utilization under a predefined threshold in order to minimize latency and drop rate. The key to achieving the desired fairness is using the gradient of the link utilization to control the sending rate. We compared MLACC to BBR (which is at least on par with the state-of-the-art ML-based techniques) as a base case in different network settings. The results show that MLACC can achieve lower and more stable end-to-end latency (25% to 52% latency saving). It also significantly reduces packet drop rates while attaining a higher fairness level. The only cost for these advantages is a small throughput reduction of less than 3.5%.