Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 28Citation - Scopus: 56HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing(IEEE-Inst Electrical Electronics Engineers Inc, 2023-11-01) Golec, Muhammed; Gill, Sukhpal Singh; Parlikad, Ajith Kumar; Uhlig, SteveHeart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest artificial intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP)-based serverless computing environment due to its advantages, such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters, such as accuracy, precision, recall, F-Score, and AUC. Experimental results demonstrate that the light gradient boosting machine model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality-of-Service (QoS) parameters, such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency.Article Citation - WoS: 8Citation - Scopus: 8Edgebus: Co-Simulation Based Resource Management for Heterogeneous Mobile Edge Computing Environments(Elsevier, 2024-12) Ali, Babar; Golec, Muhammed; Gill, Sukhpal Singh; Wu, Huaming; Cuadrado, Felix; Uhlig, SteveKubernetes 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 EdgeBus(1), 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.Article Citation - WoS: 85Citation - Scopus: 140Edge AI: A Taxonomy, Systematic Review and Future Directions(Springer, 2024-10-18) Gill, Sukhpal Singh; Golec, Muhammed; Hu, Jianmin; Xu, Minxian; Du, Junhui; Wu, Huaming; Uhlig, SteveEdge 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.Article Citation - Scopus: 9Captain: A Testbed for Co-Simulation of Scalable Serverless Computing Environments for AIoT Enabled Predictive Maintenance in Industry 4.0(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Golec, Muhammed; Wu, Huaming; Ozturac, Ridvan; Kumar Parlikad, Ajith; Cuadrado Latasa, Felix; Gill, Sukhpal Singh; Uhlig, Steve; Cuadrado, Felix; Singh Gill, SukhpalThe massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with artificial intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs random forest (RF) and light gradient-boosting machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module’s performance using a predictive maintenance-based real-world IIoT application and the AI module’s performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 ms, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO<inf>2</inf>. The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 21Citation - Scopus: 22ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments(IEEE-Inst Electrical Electronics Engineers Inc, 2024-11) Golec, Muhammed; Gill, Sukhpal Singh; Cuadrado, Felix; Parlikad, Ajith Kumar; Xu, Minxian; Wu, Huaming; Uhlig, SteveServerless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and CO2 emission amount of these models are evaluated and compared for the training and prediction phases.Article Citation - WoS: 130Citation - Scopus: 218AI-Based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions(Elsevier, 2023-04) Iftikhar, Sundas; Gill, Sukhpal Singh; Song, Chenghao; Xu, Minxian; Aslanpour, Mohammad Sadegh; Toosi, Adel N.; Uhlig, SteveResource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing.