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arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. 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 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 Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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HUNTER: AI based holistic resource management for sustainable cloud computing

arXiv

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

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 energy-efficient 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 multi-objective 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. © 2021, CC BY.

关键词： Energy efficiency

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A blockchain-based trust management framework with verifiable interactions

arXiv

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arXiv 2021年

作者： Pala, Shantanu Hill, Ambrose Rabehaja, Tahiry Hitchens, Michael School of Computer Science Queensland University of Technology BrisbaneQLD4000 Australia School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia Distributed Sensing Systems Group CSIRO’s Data61 BrisbaneQLD4069 Australia Risk Frontiers SydneyNSW2065 Australia Department of Computing Macquarie University SydneyNSW2109 Australia

There has been tremendous interest in the development of formal trust models and metrics through the use of analytics (e.g., Belief Theory and Bayesian models), logics (e.g., Epistemic and Subjective Logic) and other mathematical models. The choice of trust metric will depend on context, circumstance and user requirements and there is no single best metric for use in all circumstances. Where different users require different trust metrics to be employed the trust score calculations should still be based on all available trust evidence. Trust is normally computed using past experiences but, in practice (especially in centralised systems), the validity and accuracy of these experiences are taken for granted. In this paper, we provide a formal framework and practical blockchain-based implementation that allows independent trust providers to implement different trust metrics in a distributed manner while still allowing all trust providers to base their calculations on a common set of trust evidence. Further, our design allows experiences to be provably linked to interactions without the need for a central authority. This leads to the notion of evidence-based trust with provable interactions. Leveraging blockchain allows the trust providers to offer their services in a competitive manner, charging fees while users are provided with payments for recording experiences. Performance details of the blockchain implementation are provided. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

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. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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A bilateral game approach for task outsourcing in multi-access edge computing

arXiv

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arXiv 2020年

作者： Xiao, Zheng He, Dan Chen, Yu Chronopoulos, Anthony Theodore Dustdar, Schahram Du, Jiayi College of Computer Science and Electronic Engineering Hunan University Hunan410082 China Department of Computer Science University of Texas San AntonioTX78249 United States Dept Computer Engineering Informatics University of Patras Rio26500 Greece Distributed Systems Group Vienna University of Technology Vienna Austria

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of edge servers’ resource allocation has been widely studied. Most of previous works focus on a single edge server (ES) serving multiple terminal entities (TEs), which restricts their access to sufficient resources. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. Game theory can be used as an effective tool to realize the interests of two or more conflicting individuals in the trading market. Therefore, we propose a bilateral game framework among multiple ESs and multiple TEs by modeling the task outsourcing problem as two noncooperative games: the supplier and customer side games. In the first game, the supply function bidding mechanism is employed to model the ESs’ profit maximization problem. The ESs submit their bids to the scheduler, where the computing service price is computed and sent to the TEs. While in the second game, TEs determine the optimal demand profiles according to ESs’ bids to maximize their payoff. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. A distributed task outsourcing algorithm (DTOA) is designed to determine the equilibrium. Simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoff, as well as flattening the peak and off-peak load. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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Thermosim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments

arXiv

引用

arXiv 2020年

作者： Gill, Sukhpal Singh Tuli, Shreshth Toosi, Adel Nadjaran Cuadrado, Felix Garraghan, Peter Bahsoon, Rami Lutfiyya, Hanan Sakellariou, Rizos Rana, Omer Dustdar, Schahram Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Delhi India Faculty of Information Technology Monash University ClaytonVIC Australia Spain School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Department of Computer Science University of Western Ontario LondonON Canada School of Computer Science University of Manchester Oxford Road ManchesterUK United Kingdom School of Computer Science and Informatics Cardiff University CardiffUK United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Laboratory School of Computing and Information Systems University of Melbourne Australia

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behaviour of CDCs. There is a need for a thermal-aware framework to simulate and model the behaviour of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modelling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyse the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modelling and simulating the thermal behaviour of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy. Copyright © 2020, The Authors. All rights rese

关键词： Cloud computing

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Self-Awareness for Autonomous systems

引用

PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Entrans: Leveraging kinetic energy harvesting signal for transportation mode detection

arXiv

引用

arXiv 2018年

作者： Lany, Guohao Xu, Weitao Ma, Dong Khalifa, Sara Hassan, Mahbub Hu, Wen Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States College of Computer Science and Software Engineering Shenzhen University China School of Computer Science and Engineering University of New South Wales Sydney Australia Distributed Sensing Systems research group Data61 CSIRO Australia

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 hours of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Accelerometers

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Using English as pivot to extract Persian-Italian parallel sentences from non-parallel corpora

arXiv

引用

arXiv 2017年

作者： Ansari, Ebrahim Sadreddini, M.H. Sheikhalishahi, Mostafa Wallace, Richard Alimardani, Fatemeh Zanjan Iran Department of Computer Science and Engineering Shiraz University Shiraz Iran Department of Mathematics and Informatics University of Calabria Rende Italy Distributed Systems Architecture Research Group Complutense University Madrid Spain

The effectiveness of a statistical machine translation system (SMT) is very dependent upon the amount of parallel corpus used in the training phase. For low-resource language pairs there are not enough parallel corpora to build an accurate SMT. In this paper, a novel approach is presented to extract bilingual Persian-Italian parallel sentences from a non-parallel (comparable) corpus. In this study, English is used as the pivot language to compute the matching scores between source and target sentences and candidate selection phase. Additionally, a new monolingual sentence similarity metric, Normalized Google Distance (NGD) is proposed to improve the matching process. Moreover, some extensions of the baseline system are applied to improve the quality of extracted sentences measured with BLEU. Experimental results show that using the new pivot based extraction can increase the quality of bilingual corpus significantly and consequently improves the performance of the Persian-Italian SMT system. Copyright © 2017, The Authors. All rights reserved.

关键词： computer aided language translation

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Rapid mobile development with ARC: Application framework for robust communications for disaster risk reduction and management 12

Rapid mobile development with ARC: Application framework for...

引用

12th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2016

作者： Santos, Jan Vincent Torres, Danah Veronica Villanoy, Monica Kathrina Festin, Cedric Angelo Doctor, John Norman Ocampo, Roel Networks and Distributed Systems Group Department of Computer Science University of the Philippines Philippines Computer Networks Laboratory Electrical and Electronics Engineering Institute University of the Philippines Philippines

ISBN: (纸本)9781509007240

In disaster situations communication and power infrastructure could be damaged, disrupting their associated services. This could delay relief efforts and cost more lives. In situations where connections are damaged and unreliable, peer-to-peer (P2P) and delay-tolerant networking (DTN) technologies can help provide alternative communication modes particularly in support of rescue and relief operations. Combining the benefits of the store-and-forward capability of DTN and the data propagation techniques of Wi-Fi Direct, we have created a flexible framework called ARC which allows users to create applications that could meet their specific requirements, particularly those in support of emergency communications. To demonstrate our framework, we created a simple application using ARC, and used this application to evaluate its DTN and Wi-Fi Direct group recovery capabilities. © 2016 IEEE.

关键词： Wireless local area networks (WLAN)

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