Browsing by Author "Golec, Muhammed"

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Citation - WoS: 16
Citation - Scopus: 17
ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments
(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Golec, Muhammed; Gill, Sukhpal Singh; Cuadrado, Felix; Parlikad, Ajith Kumar; Xu, Minxian; Wu, Huaming; Uhlig, Steve
Serverless 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.
Citation - WoS: 17
Citation - Scopus: 26
BlockFaas: Blockchain-Enabled Serverless Computing Framework for AI-Driven IoT Healthcare Applications
(Springer, 2023) Golec, Muhammed; Gill, Sukhpal Singh; Golec, Mustafa; Xu, Minxian; Ghosh, Soumya K.; Kanhere, Salil S.; Uhlig, Steve
With the development of new sensor technologies, Internet of Things (IoT)-based healthcare applications have gained momentum in recent years. However, IoT devices have limited resources, making them incapable of executing large computational operations. To solve this problem, the serverless paradigm, with its advantages such as dynamic scalability and infrastructure management, can be used to support the requirements of IoT-based applications. However, due to the heterogeneous structure of IoT, user trust must also be taken into account when providing this integration. This problem can be overcome by using a Blockchain that guarantees data immutability and ensures that any data generated by the IoT device is not modified. This paper proposes a BlockFaaS framework that supports dynamic scalability and guarantees security and privacy by integrating a serverless platform and Blockchain architecture into latency-sensitive Artificial Intelligence (AI)-based healthcare applications. To do this, we deployed the AIBLOCK framework, which guarantees data immutability in smart healthcare applications, into HealthFaaS, a serverless-based framework for heart disease risk detection. To expand this framework, we used high-performance AI models and a more efficient Blockchain module. We use the Transport Layer Security (TLS) protocol in all communication channels to ensure privacy within the framework. To validate the proposed framework, we compare its performance with the HealthFaaS and AIBLOCK frameworks. The results show that BlockFaaS outperforms HealthFaaS with an AUC of 4.79% and consumes 162.82 millijoules less energy on the Blockchain module than AIBLOCK. Additionally, the cold start latency value occurring in Google Cloud Platform, the serverless platform into which BlockFaaS is integrated, and the factors affecting this value are examined.
Citation - Scopus: 5
Captain: 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) Golec, Muhammed; Wu, Huaming; Ozturac, Ridvan; Kumar Parlikad, Ajith; Cuadrado Latasa, Felix; Gill, Sukhpal Singh; Uhlig, Steve
The 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 gCO2. 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.
Citation - WoS: 12
Citation - Scopus: 12
Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions
(Assoc Computing Machinery, 2025) Golec, Muhammed; Walia, Guneet kaur; Kumar, Mohit; Cuadrado, Felix; Gill, Sukhpal singh; Uhlig, Steve
Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on cold start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence/Machine Learning-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.
Citation - Scopus: 13
CoviDetector: A Transfer Learning-Based Semi Supervised Approach to Detect COVID-19 Using CXR Images
(Elsevier B.V., 2023) Chowdhury, Deepraj; Das, Anik; Dey, Ajoy; Banerjee, Soham; Golec, Muhammed; Kollias, Dimitrios; Arya, Rajesh Chand
COVID-19 was one of the deadliest and most infectious illnesses of this century. Research has been done to decrease pandemic deaths and slow down its spread. COVID-19 detection investigations have utilised Chest X-ray (CXR) images with deep learning techniques with its sensitivity in identifying pneumonic alterations. However, CXR images are not publicly available due to users’ privacy concerns, resulting in a challenge to train a highly accurate deep learning model from scratch. Therefore, we proposed CoviDetector, a new semi-supervised approach based on transfer learning and clustering, which displays improved performance and requires less training data. CXR images are given as input to this model, and individuals are categorised into three classes: (1) COVID-19 positive; (2) Viral pneumonia; and (3) Normal. The performance of CoviDetector has been evaluated on four different datasets, achieving over 99% accuracy on them. Additionally, we generate heatmaps utilising Grad-CAM and overlay them on the CXR images to present the highlighted areas that were deciding factors in detecting COVID-19. Finally, we developed an Android app to offer a user-friendly interface. We release the code, datasets and results’ scripts of CoviDetector for reproducibility purposes; they are available at: https://github.com/dasanik2001/CoviDetector © 2024 Elsevier B.V., All rights reserved.
Citation - WoS: 64
Citation - Scopus: 103
Edge AI: A Taxonomy, Systematic Review and Future Directions
(Springer, 2025) Gill, Sukhpal Singh; Golec, Muhammed; Hu, Jianmin; Xu, Minxian; Du, Junhui; Wu, Huaming; Uhlig, Steve
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.
Citation - Scopus: 41
EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments
(Elsevier Ltd, 2024) Nandhakumar, Aadharsh Roshan; Baranwal, Ayush; Choudhary, Priyanshukumar; Golec, Muhammed; Gill, Sukhpal Singh
To meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios. © 2023 Elsevier B.V., All rights reserved.
Citation - WoS: 7
Citation - Scopus: 7
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
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 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.
Citation - WoS: 28
Citation - Scopus: 55
HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Golec, Muhammed; Gill, Sukhpal Singh; Parlikad, Ajith Kumar; Uhlig, Steve
Heart 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.
Citation - WoS: 8
Citation - Scopus: 8
Priceless: Privacy Enhanced AI-Driven Scalable Framework for IoT Applications in Serverless Edge Computing Environments
(John Wiley & Sons Ltd, 2025) Golec, Muhammed; Golec, Mustafa; Xu, Minxian; Wu, Huaming; Gill, Sukhpal Singh; Uhlig, Steve
Serverless edge computing has emerged as a new paradigm that integrates the serverless and edge computing. By bringing processing power closer to the edge of the network, it provides advantages such as low latency by quickly processing data for time-sensitive Internet of Things (IoT) applications. Additionally, serverless edge computing also brings inherent problems of edge and serverless computing such as cold start, security and privacy that are still waiting to be solved. In this paper, we propose a new Blockchain-based AI-driven scalable framework called PRICELESS, to offer security and privacy in serverless edge computing environments while performing cold start prediction. In PRICELESS framework, we used deep reinforcement learning for the cold start latency prediction. For experiments, a cold start dataset is created using a heart disease risk-based IoT application and deployed using Google Cloud Functions. Experimental results show the additional delay that the blockchain module brings to cold start latency and its impact on cold start prediction performance. Additionally, the performance of PRICELESS is compared with the current state-of-the-art method based on energy cost, computation time and cold start prediction. Specifically, it has been observed that PRICELESS causes 19 ms of external latency, 358.2 watts for training, and 3.6 watts for prediction operations, resulting in additional energy consumption at the expense of security and privacy.
Citation - WoS: 6
Citation - Scopus: 7
Prokube: Proactive Kubernetes Orchestrator for Inference in Heterogeneous Edge Computing
(Wiley, 2025) Ali, Babar; Golec, Muhammed; Gill, Sukhpal Singh; Cuadrado, Felix; Uhlig, Steve
Deep neural network (DNN) and machine learning (ML) models/ inferences produce highly accurate results demanding enormous computational resources. The limited capacity of end-user smart gadgets drives companies to exploit computational resources in an edge-to-cloud continuum and host applications at user-facing locations with users requiring fast responses. Kubernetes hosted inferences with poor resource request estimation results in service level agreement (SLA) violation in terms of latency and below par performance with higher end-to-end (E2E) delays. Lifetime static resource provisioning either hurts user experience for under-resource provisioning or incurs cost with over-provisioning. Dynamic scaling offers to remedy delay by upscaling leading to additional cost whereas a simple migration to another location offering latency in SLA bounds can reduce delay and minimize cost. To address this cost and delay challenges for ML inferences in the inherent heterogeneous, resource-constrained, and distributed edge environment, we propose ProKube, which is a proactive container scaling and migration orchestrator to dynamically adjust the resources and container locations with a fair balance between cost and delay. ProKube is developed in conjunction with Google Kubernetes Engine (GKE) enabling cross-cluster migration and/ or dynamic scaling. It further supports the regular addition of freshly collected logs into scheduling decisions to handle unpredictable network behavior. Experiments conducted in heterogeneous edge settings show the efficacy of ProKube to its counterparts cost greedy (CG), latency greedy (LG), and GeKube (GK). ProKube offers 68%, 7%, and 64% SLA violation reduction to CG, LG, and GK, respectively, and it improves cost by 4.77 cores to LG and offers more cost of 3.94 to CG and GK. ProKube is a proactive container scaling and migration orchestrator to dynamically adjust the resources and container locations with a fair balance between cost and delay for ML inferences in the inherent heterogeneous, resource-constrained, and distributed edge environments. image