- Dr. Sukhpal Singh Gill is a Lecturer (Assistant Professor) in Cloud Computing at School of Electronic Engineering and... moreDr. Sukhpal Singh Gill is a Lecturer (Assistant Professor) in Cloud Computing at School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London (QMUL), UK and he is a member of Network Research Group. Prior to this, Dr. Gill has held positions as a Research Associate @ Evolving Distributed Systems Lab at the School of Computing and Communications, Lancaster University, UK and also as a Postdoctoral Research Fellow at the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Australia. He was awarded Fellow of the Higher Education Academy (FHEA) in 2022 after passing PGCAP/PGCert with Distinction. He has published his PGCAP/PGCert work in highly-ranked Education Conferences and Journals such as IEEE EDUCON (top conference for education papers with acceptance rate 26%), Wiley Computer Applications in Engineering Education (Impact Factor = 2.1) and IT NOW – British Computer Society (BCS). Before joining CLOUDS Lab, Dr. Gill also worked in Computer Science and Engineering Department of Thapar University, India, as a Lecturer. Dr. Gill received his Bachelor’s degree in Computer Science and Engineering from Punjab Technical University with Distinction in 2010. Then, he obtained the Degree of Master of Engineering in Software Engineering (Gold Medalist), as well as a Doctoral Degree specialization in Autonomic Cloud Computing from Thapar University. He was a DST (Department of Science & Technology) Inspire Fellow during Doctorate and worked as a Senior Research Fellow (Professional) on DST Project, Government of India. Dr. Gill was a research visitor at Monash University, University of Manitoba, University of Manchester and Imperial College London. He was a recipient of several awards, including the Distinguished Reviewer Award from Software: Practice and Experience (Wiley), 2018, and Best Paper Award AusPDC at ACSW 2021 and has also served as the PC member for venues such as IEEE PerCom, UCC, CCGRID, CLOUDS, ICFEC, AusPDC. His one review paper has been nominated and selected for the ACM 21st annual Best of Computing Notable Books and Articles as one of the notable items published in computing – 2016. He has co-authored 100+ peer-reviewed papers (with H-index 35+ as per Google Scholar) and has published in prominent international journals and conferences such as IEEE TCC, IEEE TSC, IEEE TII, IEEE TNSM, IEEE IoT Journal, Elsevier JSS/FGCS, IEEE/ACM UCC and IEEE CCGRID. Dr. Gill served as a Guest Editor for SPE (Wiley), JCC Springer Journal, Sustainability Journal (MDPI) and Sensors Journal (MDPI). He is a regular reviewer for IEEE TPDS, IEEE TSC, IEEE TNSE, IEEE TSC, ACM CSUR and Wiley SPE. Dr. Gill has reviewed 570+ research articles of high ranked journals and prestigious conferences as per Web of Science. He has edited a research books for Elsevier, Springer and CRC Press. Dr. Gill is serving as an Associate Editor in IEEE IoT Journal, Elsevier IoT Journal, Wiley ETT Journal and IET Networks Journal. He is a professional member of ACM. His name appears in the list of the World’s Top 2% of Scientists released by Stanford University and Elsevier BV (2022). Dr. Gill wrote articles for international magazines such as Ars Technica, Tech Monitor, Cutter Consortium and ICT Academy. His research interests include Cloud Computing, Fog Computing, Software Engineering, Internet of Things and Energy Efficiency. For further information, please Contact Dr. Gill at s.s.gill@qmul.ac.ukedit
Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning. Large Language Models (LLMs) are emerging as potent tools increasingly capable of performing... more
Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning. Large Language Models (LLMs) are emerging as potent tools increasingly capable of performing human-level tasks. The recent development in the form of Generative Pre-trained Transformer 4 (GPT-4) and its demonstrated success in tasks complex to humans exam and complex problems has led to an increased confidence in the LLMs to become perfect instruments of intelligence. Although GPT-4 report has shown performance on some cognitive psychology tasks, a comprehensive assessment of GPT-4, via the existing well-established datasets is required. In this study, we focus on the evaluation of GPT-4’s performance on a set of cognitive psychology datasets such as CommonsenseQA, SuperGLUE, MATH and HANS. In doing so, we understand how GPT-4 processes and integrates cognitive psychology with contextual information, providing insight into the underlying cognitive processes that enable its ability to generate the responses. We show that GPT-4 exhibits a high level of accuracy in cognitive psychology tasks relative to the prior state-of-the-art models. Our results strengthen the already available assessments and confidence on GPT-4’s cognitive psychology abilities. It has significant potential to revolutionise the field of Artificial Intelligence (AI), by enabling machines to bridge the gap between human and machine reasoning.
Research Interests: Psychology, Cognitive Psychology, Cognitive Science, Social Psychology, Developmental Psychology, and 15 moreArtificial Intelligence, Philosophy Of Language, Natural Language Processing, Reinforcement Learning, Machine Learning, Educational Psychology, Support Vector Machines, Statistical machine learning, Cloud Computing, Computational Linguistics & NLP, Deep Learning, Natural Language Processing(NLP), GPT-3, ChatGPT, and Large language models
ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of... more
ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit
Research Interests: Artificial Intelligence, Education, Teacher Education, Machine Learning, Higher Education, and 9 moreApplications of Machine Learning, Future of artificial intelligence, Early career academics, Artificial Intelligence in Education, Academics, Chatbot, Internet of Things (IoT), ChatGPT, and ChatGPT API
Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years. Of these, the development of virtual assistants has accelerated greatly in the past few years, with ChatGPT becoming a... more
Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years. Of these, the development of virtual assistants has accelerated greatly in the past few years, with ChatGPT becoming a prominent AI language model. In this study, we examine the foundations, vision, research challenges of ChatGPT. This article investigates into the background and development of the technology behind it, as well as its popular applications. Moreover, we discuss the advantages of bringing everything together through ChatGPT and Internet of Things (IoT). Further, we speculate on the future of ChatGPT by considering various possibilities for study and development, such as energy-efficiency, cybersecurity, enhancing its applicability to additional technologies (Robotics and Computer Vision), strengthening human-AI communications, and bridging the technological gap. Finally, we discuss the important ethics and current trends of ChatGPT.
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Resource 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... more
Resource 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.
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Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters to support the ever-increasing demand for their computational and application services. However, the financial and carbon footprint related... more
Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters to support the ever-increasing demand for their computational and application services. However, the financial and carbon footprint related costs of running such large infrastructure negatively impacts the sustainability of cloud services. Most of existing efforts primarily focus on minimizing the energy consumption of servers. In this paper, we devise a conceptual model and practical design guidelines for holistic management of all resources (including servers, networks, storage, cooling systems) to improve the energy efficiency and reduce carbon footprints in Cloud Data Centers (CDCs). Furthermore, we discuss the intertwined relationship between energy and reliability for sustainable cloud computing, where we highlight the associated research issues. Finally, we propose a set of future research directions in the field and setup grounds for further practical developments.
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Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters to support the ever-increasing demand for their computational and application services. However, the financial and carbon footprint related... more
Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters to support the ever-increasing demand for their computational and application services. However, the financial and carbon footprint related costs of running such large infrastructure negatively impacts the sustainability of cloud services. Most of existing efforts primarily focus on minimizing the energy consumption of servers. In this paper, we devise a conceptual model and practical design guidelines for holistic management of all resources (including servers, networks, storage, cooling systems) to improve the energy efficiency and reduce carbon footprints in Cloud Data Centers (CDCs). Furthermore, we discuss the intertwined relationship between energy and reliability for sustainable cloud computing, where we highlight the associated research issues. Finally, we propose a set of future research directions in the field and setup grounds for further practical developments.
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Research Interests: Information Systems, Computer Science, Renewable Energy, Energy, Clouds, and 15 moreEnergy and Environment, Renewable energy resources, Environmental Sustainability, Energy efficiency, Cloud Computing, Carbon Footprint, Computer Software, Cloud, Resource Management, CloudSim, Power System Reliabilty, Energy Efficiency, Holistic Management, Cloud Computing and Virtualization, and Cloudsim Scheduling Examples
Research Interests: Information Systems, Computer Science, Renewable Energy, Energy, Clouds, and 15 moreEnergy and Environment, Renewable energy resources, Environmental Sustainability, Energy efficiency, Cloud Computing, Carbon Footprint, Computer Software, Cloud, Resource Management, CloudSim, Power System Reliabilty, Energy Efficiency, Holistic Management, Cloud Computing and Virtualization, and Cloudsim Scheduling Examples
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Research Interests: Information Systems, Computer Science, The Internet of Things, Internet of Things, Cloud Computing, and 15 moreComputer Software, Smart Home Technology, Smart City, Smart Home, Big Data, Smart Homes, Cloud, Internet of Things (IoT), Big Data Analytics, Systems Software, Fog Computing, Application of Fog Computing in Internet of Things, Edge Computing, Smart Cloud, and Challenges of IoT Fog
The cloud-computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT)--based applications, the use of cloud services is increasing... more
The cloud-computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT)--based applications, the use of cloud services is increasing exponentially. The next generation of cloud computing must be energy efficient and sustainable to fulfill end-user requirements, which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The use of a large number of cloud datacenters increases cost as well as carbon footprints, which further affects the sustainability of cloud services. In this article, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. The current research on sustainable cloud computing is organized into se...
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Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data... more
Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.
Research Interests: Information Systems, Cognitive Science, Computer Science, Digital Technology, Agriculture, and 15 moreInternet of Things, Cloud Computing, Business and Management, Big Data, Cloud, Big Data Analytics, IOT, Agricutlure Management, Big Data Technologies, Cloud Computing and Virtualization, Big Data Applications, Application of Big Data, Big Data and Cloud Computing, Digital India, and Digital India initiative
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Research Interests: Computer Science, Artificial Intelligence, Machine Learning, Classification (Machine Learning), Clustering and Classification Methods, and 15 moreApplications of Machine Learning, Clouds, Clustering Algorithms, Cloud Computing, QoS and QoE, Deep Learning, Clustering, Cluster Analysis, Cloud, Workload, Artifical Neural Networks, Cloud Scheduling, Job Scheduling on Grid Computing and Cloud Computing, Cloud Computing and Virtualization, and Resource Scheduling In Cloud Computing
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Cloud based development is a challenging task for several software engineering projects, especially for those which needs development with reusability. Present time of cloud computing is allowing new professional models for using the... more
Cloud based development is a challenging task for several software engineering projects, especially for those which needs development with reusability. Present time of cloud computing is allowing new professional models for using the software development. The expected upcoming trend of computing is assumed to be this cloud computing because of speed of application deployment, shorter time to market, and lower cost of operation. Until Cloud Co mputing Reusability Model is considered a fundamental capability, the speed of developing services is very slow. Th is paper spreads cloud computing with component based development named Cloud Co mputing Reusability Model (CCR) and enable reusability in cloud computing. In this paper Cloud Co mputing Reusability Model has been proposed. The model has been validated by Cloudsim an d experimental result shows that reusability based cloud computing approach is effective in minimizing cost and time to market.
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Research Interests: Computer Science, Distributed Computing, Clouds, Cloud Computing, Computer Software, and 13 moreResource Provisioning, Quality of Service, Cloud Resource Management, Cloud, Resource Management, Origin of Cloud Computing, Workflow QoS, Quality of Service (QoS), Resource provisioning in cloud computing, Cloud Computing and Virtualization, Electrical And Electronic Engineering, Dynamic resource provisioning, and Computers and Electrical Engineering
Research Interests: Computer Science, Software Engineering, Climate Change, Machine Learning, Security, and 15 moreEnergy, The Internet of Things, Evolution, Internet of Things, Cloud Computing, Artifical Intelligence, Deep Learning, Cloud, IOT, Cloud Computing and Virtualization, Elsevier, Philosophy of Mind and Artificial Intelligence & AI, Blockchains, Fog Computing, and Blockchain
Teaching and research are the two sides of a coin; both are very important for an academician. As per the current demand of the modern education system, teaching and research would be helpful to build a long and sustainable career in... more
Teaching and research are the two sides of a coin; both are very important for an academician. As per the current demand of the modern education system, teaching and research would be helpful to build a long and sustainable career in academics. To move ahead smoothly, there is a necessity to carry out research on whatever an academician is teaching. This methodology can help academicians to make a strong connection between research and teaching. Further, it helps students to learn the ongoing research trends in their respective fields.
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The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be... more
The outbreak of COVID-19 Coronavirus, namely SARS-CoV-2, has created a calamitous situation throughout the world. The cumulative incidence of COVID-19 is rapidly increasing day by day. Machine Learning (ML) and Cloud Computing can be deployed very effectively to track the disease, predict growth of the epidemic and design strategies and policies to manage its spread. This study applies an improved mathematical model to analyse and predict the growth of the epidemic. An ML-based improved model has been applied to predict the potential threat of COVID-19 in countries worldwide. We show that using iterative weighting for fitting Generalized Inverse Weibull distribution, a better fit can be obtained to develop a prediction framework. This has been deployed on a cloud computing platform for more accurate and real-time prediction of the growth behavior of the epidemic. A data driven approach with higher accuracy as here can be very useful for a proactive response from the government and citizens. Finally, we propose a set of research opportunities and setup grounds for further practical applications.