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CLAWER: Context-aware Cloud-Fog based Workflow Management Framework for Health Emergency Services

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CLAWER: Context-aware Cloud-Fog based Workflow Management Fr...

IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Shreya Ghosh Jaydeep Das Soumya K Ghosh Rajkumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9781728160955

ISBN: (纸本)9781728196497

With the major development of sensor technologies and advancements of communication network infrastructures, there is a growing interest to add more intelligence in the e-health monitoring for facilitating an effective healthcare system. While IoT devices are capable of continuous health-parameter sensing and providing notifications to the user, an effective business process management (BPM) facilitates effective system integration and data processing workflow. This paper proposes an efficient framework for managing emergency situations (specifically, health-related) through the analysis of heterogeneous data sources. The proposed framework, named CLAWER (CLoud-Fog bAsed Workflow for Emergency seRvice) aims to bridge the gap between process management and data analytics by providing an automated workflow for personalized health-monitoring and efficient recommendation system. Here, the IoT devices are used for collecting the movement and health data. The smart phone can act as an edge device to acquire data with user movement information. The accumulated data is initially processed inside the fog device, and finally the analysis and recommendations are generated by the cloud. In this paper the indoor health-status of the users are analysed in small cell cloud enhanced eNode B, which is used as fog device. The generated recommendations are stored in the fog device to provide the recommendations to the users with low latency and in timely manner. The experimental analysis of CLAWER yields better precision and recall values than the existing methods.

关键词： Cloud computing Roads Medical services Markov processes Monitoring Delays Biomedical monitoring

Performance and energy-aware bi-objective tasks scheduling for cloud data centers

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arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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

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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

Enhancing data harvesting systems: Performance quantification of Cloud-Edge-sensor networks using queueing theory

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ICT Express 2025年

作者： Jose Wanderlei Rocha Eder Gomes Vandirleya Barbosa Arthur Sabino Luiz Nelson Lima Gustavo Callou Francisco Airton Silva Eunmi Choi Tuan Anh Nguyen Dugki Min Jae-Woo Lee Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piaui (UFPI) Picos Piaui R. Cicero Duarte No. 905 - Junco 64607-670 Brazil School of Software College of Computer Science Kookmin University Seoul 02707 South Korea Department of Software Engineering Faculty of Information Technology Ho Chi Minh City University of Industry and Trade (HUIT) Ho Chi Minh City 700000 Viet Nam Department of Artificial Intelligence Graduate School Konkuk University Seoul 05029 South Korea Department of Computer Science and Engineering College of Engineering Konkuk University Seoul 05029 South Korea Department of Aerospace Engineering Konkuk University Seoul 05029 South Korea

This study investigates a Cloud-Edge-sensors infrastructure using M/M/c/K queuing theory to analyze agricultural data systems’ performance. It focuses on optimizing data handling and evaluates the system configuration impacts on performance. The model significantly enhances efficiency and scalability, minimizing the need for extensive physical infrastructure. Analysis shows over 90% utilization in both layers, highlighting the model’s applicability to various IoT applications. The M/M/c/K queuing model addresses scalability and real-time data processing challenges in agricultural cloud–edge-sensor networks, improving over traditional methods lacking dynamic scalability. Designed for optimized resource use and reduced data handling delays, this model proves crucial in precision agriculture, where timely data is essential for decision-making. Its versatility extends to various agricultural applications requiring efficient real-time analysis and resource management.

关键词： Data harvesting Agricultural monitoring system Queuing model Sensors Performance

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

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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

Foundational Competencies and Responsibilities of a Research Software Engineer

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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： computer aided software engineering

MovCloud: A Cloud-enabled Framework to Analyse Movement Behaviors

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MovCloud: A Cloud-enabled Framework to Analyse Movement Beha...

IEEE International Conference on Cloud Computing technology and science

作者： Shreya Ghosh Soumya K Ghosh Rajkumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Indian Institute of Technology Kharagpur Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) Lab The University of Melbourne Australia

Understanding human interests and intents from movement data are fundamental challenges for any location-based service. With the pervasiveness of sensor embedded smartphones and wireless networks and communication, the availability of spatio-temporal mobility trace (timestamped location information) is increasingly growing. Analysing these huge amount of mobility data is another major concern. This paper proposes a cloud-based framework named MovCloud to efficiently manage and analyse mobility data. Specifically, the framework presents a hierarchical indexing schema to store trajectory data in different spatio-temporal resolution, clusters the trajectories based on semantic movement behaviour instead of only raw latitude, longitude point and resolves mobility queries using MapReduce paradigm. MovCloud is implemented over Google Cloud Platform (GCP) and an extensive set of experiments on real-life data yield the effectiveness of the proposed framework. MovCloud has achieved ～ 28% better clustering accuracy and also executed three times faster than the baseline methods.

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Geo-Cloudlet: Time and Power Efficient Geospatial Query Resolution using Cloudlet

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Geo-Cloudlet: Time and Power Efficient Geospatial Query Reso...

International Conference on Advanced Computing, ICAC

作者： Jaydeep Das Anwesha Mukherjee Soumya K. Ghosh Rajkumar Buyya Advanced Technology Development Centre Indian Institute of Technology Kharagpur West Bengal India Department of Computer Science Mahishadal Raj College Purba Medinipur West Bengal India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne VIC Australia

ISBN: (数字)9781728152868

ISBN: (纸本)9781728152875

Geospatial data analysis is an emerging area of research today due to the potential to enable varied location-aware services. The existing centralized cloud-based analysis becomes time and computing-intensive for huge amount of geospatial data processing. This paper addresses the challenge of time and power-efficiency in QoS-aware geospatial query resolution. We propose a cloudlet based hierarchical paradigm, namely Geo-Cloudlet, where the cloudlets contain the geospatial data of the districts. The state and national level geospatial data are stored inside the state cloud and country cloud respectively. The query resolution is performed by either the cloudlet or by the state cloud or country cloud depending upon the geographical region related to the query. The experimental analysis illustrates that the proposed architecture Geo-Cloudlet reduces the latency up to 61.3% and power consumption up to 61.1% over the use of only remote cloud servers for geospatial query resolution.

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Self-Awareness for Autonomous systems

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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