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Sukhpal Singh Gill

Queen Mary, University of London, School of Electronic Engineering and Computer Science, Faculty Member

Lancaster University, School of Computing and Communications, Post-Doc

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Dr. 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 [email protected]

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Vladislav Mikhailov

Shivani Agrawal -XII D

Ludwig-Maximilians-Universität München

Gretchen Krueger

Leila Khalatbari

Shekoofeh Azizi

University of British Columbia

Pratyay Banerjee

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Papers by Sukhpal Singh Gill

Mind meets machine: Unravelling GPT-4’s cognitive psychology

BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2023

Cognitive psychology delves on understanding perception, attention, memory, language, problem-sol... 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.

Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

by Sukhpal Singh Gill and Rami Bahsoon

Internet of Things and Cyber-Physical Systems, 2024

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volum... 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

ChatGPT: Vision and challenges

Internet of Things and Cyber-Physical Systems, 2023

Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry i... 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.

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

by Sukhpal Singh Gill, Sundas Iftikhar, Mohammad Aslanpour, Muhammed Golec, and Schahram Dustdar

Internet of Things , 2023

Resource management in computing is a very challenging problem that involves making sequential de... 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.

Sustainable Cloud Computing: Foundations and Future Directions

ArXiv, 2018

Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters ... 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.

AI for next generation computing: Emerging trends and future directions

Internet of Things, 2022

Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on t... more Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and interdependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

IEEE Transactions on Services Computing, 2021

Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in... more Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances which increase the overall response time. Such tasks can significantly impact the system's Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore heterogeneous host or volatile task characteristics. In this paper, we propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. Our technique analyzes all tasks and hosts based on compute and network resource consumption using an Encoder Long-Short-Term-Memory (LSTM) network. The output of this network is then used to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START in a cloud environment and compare it with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler) in terms of QoS parameters such as energy consumption, execution time, resource contention, CPU utilization and SLA violation rate. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%, respectively, compared to the state-of-the-art approaches.

Sustainable Cloud Computing: Foundations and Future Directions

ArXiv, 2018

Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters ... 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.

HUNTER: AI based holistic resource management for sustainable cloud computing

Journal of Systems and Software, 2021

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for h... more The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energyefficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multiobjective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

IEEE Transactions on Services Computing, 2021

Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in... more Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances which increase the overall response time. Such tasks can significantly impact the system's Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore heterogeneous host or volatile task characteristics. In this paper, we propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. Our technique analyzes all tasks and hosts based on compute and network resource consumption using an Encoder Long-Short-Term-Memory (LSTM) network. The output of this network is then used to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START in a cloud environment and compare it with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler) in terms of QoS parameters such as energy consumption, execution time, resource contention, CPU utilization and SLA violation rate. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%, respectively, compared to the state-of-the-art approaches.

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Simulation Modelling Practice and Theory, 2021

In cloud computing environments, when a client wants to access any resources, hardware components... more In cloud computing environments, when a client wants to access any resources, hardware components, or application services, he needs to get a subscription for the same from service providers. The usages of each client are monitored over a network by service providers and later on user will be charged for the services used. Cloud service provider is responsible for providing Quality of Service to clients. As the number of client request increases in cloud environment, cloud service providers face various issues such as scheduling and allocation of resources, security, privacy and virtual machine migration. Swarm intelligence, biological systems, physical and chemical systems based metaheuristic algorithms have proved to be efficient and used to solve real world scheduling optimization problems. This review focused on the insight view of various nature-inspired metaheuristic algorithms and their comparisons on the basis of certain parameters that affects the efficiency and effectiveness of their applicability in order to schedule different tasks in cloud environment. This work facilitates comparative analysis of six metaheuristic techniques quantitatively based on scheduling parameters like makespan and resource utilization cost. The objective of this systematic review is to find the most optimal scheduling technique for solving multi criteria scheduling problem. After evaluating and comparing Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony algorithm, Crow Search Algorithm and Penguin Swarm Optimization Algorithm, it has been identified that Crow Search algorithm is the most optimal technique in terms of makespan and resource utilization cost parameters with significant improvement over others. Finally, the promising research directions has been identified.

HUNTER: AI based holistic resource management for sustainable cloud computing

Journal of Systems and Software, 2021

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for h... more The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energyefficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multiobjective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fif... more Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidationbased energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Simulation Modelling Practice and Theory, 2021

In cloud computing environments, when a client wants to access any resources, hardware components... more In cloud computing environments, when a client wants to access any resources, hardware components, or application services, he needs to get a subscription for the same from service providers. The usages of each client are monitored over a network by service providers and later on user will be charged for the services used. Cloud service provider is responsible for providing Quality of Service to clients. As the number of client request increases in cloud environment, cloud service providers face various issues such as scheduling and allocation of resources, security, privacy and virtual machine migration. Swarm intelligence, biological systems, physical and chemical systems based metaheuristic algorithms have proved to be efficient and used to solve real world scheduling optimization problems. This review focused on the insight view of various nature-inspired metaheuristic algorithms and their comparisons on the basis of certain parameters that affects the efficiency and effectiveness of their applicability in order to schedule different tasks in cloud environment. This work facilitates comparative analysis of six metaheuristic techniques quantitatively based on scheduling parameters like makespan and resource utilization cost. The objective of this systematic review is to find the most optimal scheduling technique for solving multi criteria scheduling problem. After evaluating and comparing Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony algorithm, Crow Search Algorithm and Penguin Swarm Optimization Algorithm, it has been identified that Crow Search algorithm is the most optimal technique in terms of makespan and resource utilization cost parameters with significant improvement over others. Finally, the promising research directions has been identified.

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

Operationalizing Multi-Cloud Environments, 2021

The advancements in the use of Internet of Things (IoT) devices is increasing continuously and ge... more The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively. Further, fog and edge computing paradigms are introduced to improve user service by reducing latency and response time. This chapter presents a manifesto for modern fog and edge computing systems based on the current research trends. Further, architectures and applications of fog and edge computing are explained. Moreover, research opportunities and promising future directions are presented with respect to the new paradigms, which will be helpful for practitioners, researchers and academicians to continue their research.

Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fif... more Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidationbased energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

Sustainable Cloud Computing Realization for Different Applications: A Manifesto

Digital Business, 2018

In cloud computing, an application design plays an important role and the efficient structure of ... more In cloud computing, an application design plays an important role and the efficient structure of an application can increase the energy-efficiency and sustainability of cloud datacenters. To make the infrastructure eco-friendly, energy-efficient and sustainable, there is a need for innovative applications. In this chapter, we comprehensively analyze the challenges in sustainable cloud computing and review the current developments for different applications. We propose a taxonomy of application management for sustainable cloud computing and identified research challenges. We also map the existing related studies to the taxonomy in order to identify current search gaps in the area of application management for sustainable cloud computing. Furthermore, we propose open research challenges for sustainable cloud computing based on the observations.

Sustainable Cloud Computing Realization for Different Applications: A Manifesto

Digital Business, 2018

In cloud computing, an application design plays an important role and the efficient structure of ... more In cloud computing, an application design plays an important role and the efficient structure of an application can increase the energy-efficiency and sustainability of cloud datacenters. To make the infrastructure eco-friendly, energy-efficient and sustainable, there is a need for innovative applications. In this chapter, we comprehensively analyze the challenges in sustainable cloud computing and review the current developments for different applications. We propose a taxonomy of application management for sustainable cloud computing and identified research challenges. We also map the existing related studies to the taxonomy in order to identify current search gaps in the area of application management for sustainable cloud computing. Furthermore, we propose open research challenges for sustainable cloud computing based on the observations.

Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research

Internet of Things, 2020

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog an... more Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments.

Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research

Internet of Things, 2020

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog an... more Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments.

Mind meets machine: Unravelling GPT-4’s cognitive psychology

BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2023

Cognitive psychology delves on understanding perception, attention, memory, language, problem-sol... 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.

Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

by Sukhpal Singh Gill and Rami Bahsoon

Internet of Things and Cyber-Physical Systems, 2024

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volum... 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

ChatGPT: Vision and challenges

Internet of Things and Cyber-Physical Systems, 2023

Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry i... 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.

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

by Sukhpal Singh Gill, Sundas Iftikhar, Mohammad Aslanpour, Muhammed Golec, and Schahram Dustdar

Internet of Things , 2023

Resource management in computing is a very challenging problem that involves making sequential de... 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.

Sustainable Cloud Computing: Foundations and Future Directions

ArXiv, 2018

Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters ... 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.

AI for next generation computing: Emerging trends and future directions

Internet of Things, 2022

Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on t... more Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and interdependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

IEEE Transactions on Services Computing, 2021

Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in... more Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances which increase the overall response time. Such tasks can significantly impact the system's Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore heterogeneous host or volatile task characteristics. In this paper, we propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. Our technique analyzes all tasks and hosts based on compute and network resource consumption using an Encoder Long-Short-Term-Memory (LSTM) network. The output of this network is then used to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START in a cloud environment and compare it with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler) in terms of QoS parameters such as energy consumption, execution time, resource contention, CPU utilization and SLA violation rate. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%, respectively, compared to the state-of-the-art approaches.

Sustainable Cloud Computing: Foundations and Future Directions

ArXiv, 2018

Major cloud providers such as Microsoft, Google, Facebook and Amazon rely heavily on datacenters ... 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.

HUNTER: AI based holistic resource management for sustainable cloud computing

Journal of Systems and Software, 2021

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for h... more The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energyefficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multiobjective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

IEEE Transactions on Services Computing, 2021

Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in... more Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances which increase the overall response time. Such tasks can significantly impact the system's Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore heterogeneous host or volatile task characteristics. In this paper, we propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. Our technique analyzes all tasks and hosts based on compute and network resource consumption using an Encoder Long-Short-Term-Memory (LSTM) network. The output of this network is then used to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START in a cloud environment and compare it with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler) in terms of QoS parameters such as energy consumption, execution time, resource contention, CPU utilization and SLA violation rate. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%, respectively, compared to the state-of-the-art approaches.

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Simulation Modelling Practice and Theory, 2021

In cloud computing environments, when a client wants to access any resources, hardware components... more In cloud computing environments, when a client wants to access any resources, hardware components, or application services, he needs to get a subscription for the same from service providers. The usages of each client are monitored over a network by service providers and later on user will be charged for the services used. Cloud service provider is responsible for providing Quality of Service to clients. As the number of client request increases in cloud environment, cloud service providers face various issues such as scheduling and allocation of resources, security, privacy and virtual machine migration. Swarm intelligence, biological systems, physical and chemical systems based metaheuristic algorithms have proved to be efficient and used to solve real world scheduling optimization problems. This review focused on the insight view of various nature-inspired metaheuristic algorithms and their comparisons on the basis of certain parameters that affects the efficiency and effectiveness of their applicability in order to schedule different tasks in cloud environment. This work facilitates comparative analysis of six metaheuristic techniques quantitatively based on scheduling parameters like makespan and resource utilization cost. The objective of this systematic review is to find the most optimal scheduling technique for solving multi criteria scheduling problem. After evaluating and comparing Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony algorithm, Crow Search Algorithm and Penguin Swarm Optimization Algorithm, it has been identified that Crow Search algorithm is the most optimal technique in terms of makespan and resource utilization cost parameters with significant improvement over others. Finally, the promising research directions has been identified.

HUNTER: AI based holistic resource management for sustainable cloud computing

Journal of Systems and Software, 2021

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for h... more The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energyefficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multiobjective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fif... more Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidationbased energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Simulation Modelling Practice and Theory, 2021

In cloud computing environments, when a client wants to access any resources, hardware components... more In cloud computing environments, when a client wants to access any resources, hardware components, or application services, he needs to get a subscription for the same from service providers. The usages of each client are monitored over a network by service providers and later on user will be charged for the services used. Cloud service provider is responsible for providing Quality of Service to clients. As the number of client request increases in cloud environment, cloud service providers face various issues such as scheduling and allocation of resources, security, privacy and virtual machine migration. Swarm intelligence, biological systems, physical and chemical systems based metaheuristic algorithms have proved to be efficient and used to solve real world scheduling optimization problems. This review focused on the insight view of various nature-inspired metaheuristic algorithms and their comparisons on the basis of certain parameters that affects the efficiency and effectiveness of their applicability in order to schedule different tasks in cloud environment. This work facilitates comparative analysis of six metaheuristic techniques quantitatively based on scheduling parameters like makespan and resource utilization cost. The objective of this systematic review is to find the most optimal scheduling technique for solving multi criteria scheduling problem. After evaluating and comparing Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony algorithm, Crow Search Algorithm and Penguin Swarm Optimization Algorithm, it has been identified that Crow Search algorithm is the most optimal technique in terms of makespan and resource utilization cost parameters with significant improvement over others. Finally, the promising research directions has been identified.

A Manifesto for Modern Fog and Edge Computing: Vision, New Paradigms, Opportunities, and Future Directions

Operationalizing Multi-Cloud Environments, 2021

The advancements in the use of Internet of Things (IoT) devices is increasing continuously and ge... more The advancements in the use of Internet of Things (IoT) devices is increasing continuously and generating huge amounts of data in a fast manner. Cloud computing is an important paradigm which processes and manages user data effectively. Further, fog and edge computing paradigms are introduced to improve user service by reducing latency and response time. This chapter presents a manifesto for modern fog and edge computing systems based on the current research trends. Further, architectures and applications of fog and edge computing are explained. Moreover, research opportunities and promising future directions are presented with respect to the new paradigms, which will be helpful for practitioners, researchers and academicians to continue their research.

Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations

2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fif... more Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidationbased energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided.

Sustainable Cloud Computing Realization for Different Applications: A Manifesto

Digital Business, 2018

In cloud computing, an application design plays an important role and the efficient structure of ... more In cloud computing, an application design plays an important role and the efficient structure of an application can increase the energy-efficiency and sustainability of cloud datacenters. To make the infrastructure eco-friendly, energy-efficient and sustainable, there is a need for innovative applications. In this chapter, we comprehensively analyze the challenges in sustainable cloud computing and review the current developments for different applications. We propose a taxonomy of application management for sustainable cloud computing and identified research challenges. We also map the existing related studies to the taxonomy in order to identify current search gaps in the area of application management for sustainable cloud computing. Furthermore, we propose open research challenges for sustainable cloud computing based on the observations.

Sustainable Cloud Computing Realization for Different Applications: A Manifesto

Digital Business, 2018

In cloud computing, an application design plays an important role and the efficient structure of ... more In cloud computing, an application design plays an important role and the efficient structure of an application can increase the energy-efficiency and sustainability of cloud datacenters. To make the infrastructure eco-friendly, energy-efficient and sustainable, there is a need for innovative applications. In this chapter, we comprehensively analyze the challenges in sustainable cloud computing and review the current developments for different applications. We propose a taxonomy of application management for sustainable cloud computing and identified research challenges. We also map the existing related studies to the taxonomy in order to identify current search gaps in the area of application management for sustainable cloud computing. Furthermore, we propose open research challenges for sustainable cloud computing based on the observations.

Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research

Internet of Things, 2020

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog an... more Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments.

Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research

Internet of Things, 2020

Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog an... more Optimization is an inseparable part of Cloud computing, particularly with the emergence of Fog and Edge paradigms. Not only these emerging paradigms demand reevaluating cloud-native optimizations and exploring Fog and Edge-based solutions, but also the objectives require significant shift from considering only latency to energy, security, reliability and cost. Hence, it is apparent that optimization objectives have become diverse and lately Internet of Things (IoT)-specific born objectives must come into play. This is critical as incorrect selection of metrics can mislead the developer about the real performance. For instance, a latency-aware auto-scaler must be evaluated through latency-related metrics as response time or tail latency; otherwise the resource manager is not carefully evaluated even if it can reduce the cost. Given such challenges, researchers and developers are struggling to explore and utilize the right metrics to evaluate the performance of optimization techniques such as task scheduling, resource provisioning, resource allocation, resource scheduling and resource execution. This is challenging due to (1) novel and multi-layered computing paradigm, e.g., Cloud, Fog and Edge, (2) IoT applications with different requirements, e.g., latency or privacy, and (3) not having a benchmark and standard for the evaluation metrics. In this paper, by exploring the literature, (1) we present a taxonomy of the various real-world metrics to evaluate the performance of cloud, fog, and edge computing; (2) we survey the literature to recognize common metrics and their applications; and (3) outline open issues for future research. This comprehensive benchmark study can significantly assist developers and researchers to evaluate performance under realistic metrics and standards to ensure their objectives will be achieved in the production environments.