Browsing by Author "Ali, Babar"

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Edge AI: A Taxonomy, Systematic Review and Future Directions
(SPRINGER, 2024) Gill, Sukhpal Singh; Golec, Muhammed; Hu, Jianmin; Xu, Minxian; Du, Junhui; Wu, Huaming; Walia, Guneet Kaur; Murugesan, Subramaniam Subramanian; Ali, Babar; Kumar, Mohit; Ye, Kejiang; Verma, Prabal; Kumar, Surendra; Cuadrado, Felix; Uhlig, Steve; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Golec, Muhammed
Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years. In this article, we present a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while also examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions.
EdgeBus: Co-Simulation based resource management for heterogeneous mobile edge computing environments
(ELSEVIER, 2024) Ali, Babar; Golec, Muhammed; Gill, Sukhpal Singh; Wu, Huaming; Cuadrado, Felix; Uhlig, Steve; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Golec, Muhammed
Kubernetes has revolutionized traditional monolithic Internet of Things (IoT) applications into lightweight, decentralized, and independent microservices, thus becoming the de facto standard in the realm of container orchestration. Intelligent and efficient container placement in Mobile Edge Computing (MEC) is challenging subjected to user mobility, and surplus but heterogeneous computing resources. One solution to constantly altering user location is to relocate containers closer to the user; however, this leads to additional underutilized active nodes and increases migration's computational overhead. On the contrary, few to no migrations are attributed to higher latency, thus degrading the Quality of Service (QoS). To tackle these challenges, we created a framework named EdgeBus1, which enables the co-simulation of container resource management in heterogeneous MEC environments based on Kubernetes. It enables the assessment of the impact of container migrations on resource management, energy, and latency. Further, we propose a mobility and migration cost-aware (MANGO) lightweight scheduler for efficient container management by incorporating migration cost, CPU cores, and memory usage for container scheduling. For user mobility, the Cabspotting dataset is employed, which contains real-world traces of taxi mobility in San Francisco. In the EdgeBus framework, we have created a simulated environment aided with a real-world testbed using Google Kubernetes Engine (GKE) to measure the performance of the MANGO scheduler in comparison to baseline schedulers such as IMPALA-based MobileKube, Latency Greedy, and Binpacking. Finally, extensive experiments have been conducted, which demonstrate the effectiveness of the MANGO in terms of latency and number of migrations.
A QoS provisioning architecture of fiber wireless network based on XGPON and IEEE 802.11ac
(De Gruyter Open Ltd, 2021) Mohammadani, Khalid H.; Butt, Rizwan A.; Memon, Kamran A.; Pirzado, Azhar A.; Faheem, Muhammad; Abro, Adeel; Ali, Babar; Ain, Noor-Ul; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Muhammad, Faheem
The integration of the XGPON network with the 5G WLAN network is a suitable solution for next-generation high-speed access Internet service. We demonstrated the integration of two different standards via the QoS concept. Further, this work also presents a proper mapping scheme of QoS traffic between XG-PON and fifth-generation Wi-Fi standards known as IEEE 802.11ac. The analysis assessment compares the behavior of different IEEE 802.11ac standards with XGPON with-respect-to multimedia traffic in the FiWi network. To assess the performance of the FiWi network, the OMNET++ and INET framework are used to carrying out a comparative analysis in terms of upstream (US) delay and fairness index. The study concludes that the EDCA Wi-Fi module has better performance than the DCF Wi-Fi module with the integrated XGPON system for the FiWi access network. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.