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

Procedia computer science 2022年 197卷 238-246页

作者： Huned Materwala Leila Ismail 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 Dhabi 15551 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 the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off 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.

关键词： Autonomous agents cloud computing energy-efficiency evolutionary algorithm genetic algorithm quality of service multi-objective optimization performance

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Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading 8

Secure and Privacy-Preserving Lightweight Blockchain for Ene...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila College of Information Technology United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. The current centralized energy trading system is less secure and involves large transmission losses and high overheads. The decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. This is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture. © 2021 IEEE.

关键词： Scalability

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

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18th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned 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 Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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NUMA-aware FFT-based convolution on ARMv8 many-core CPUs

arXiv

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arXiv 2021年

作者： Huang, Xiandong Wang, Qinglin Lu, Shuyu Hao, Ruochen Mei, Songzhu Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in manycore CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Comparative Analysis of Machine Learning Models for Diabetes Mellitus Type 2 Prediction

Comparative Analysis of Machine Learning Models for Diabetes...

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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Ismail, Leila Materwala, Huned United Arab Emirates University College of Information Technology Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu-Dhabi Al-Ain15551 United Arab Emirates

ISBN: (纸本)9781728176246

Diabetes is one of the top 10 causes of death worldwide. Health professionals are aiming for machine learning models to support the prognosis of diabetes for better healthcare and to put in place an effective prevention plan. In this paper, we conduct a comparative analysis of the most used machine learning models in the literature to predict the prevalence of diabetes mellitus type 2. We evaluate the models in terms of accuracy, F-measure and execution time with and without feature selection using a real-life diabetes dataset. The detailed analysis is in the paper. © 2020 IEEE.

关键词： Medical informatics

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Towards Holographic Video Communications: A Promising AI-driven Solution

arXiv

引用

arXiv 2022年

作者： Huang, Yakun Zhu, Yuanwei Qiao, Xiuquan Su, Xiang Dustdar, Schahram Zhang, Ping The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science Norwegian University of Science and Technology Gjøvik2815 Norway University of Oulu Oulu90570 Finland The Distributed Systems Group Technische Universität Wien Vienna1040 Austria

Real-time holographic video communications enable immersive experiences for next-generation video services in the future metaverse era. However, high-fidelity holographic videos require high bandwidth and significant computation resources, which exceed the transferring and computing capacity of 5G networks. This article reviews state-of-the-art holographic point cloud video (PCV) transmission techniques and highlights the critical challenges of delivering such immersive services. We further implement a preliminary prototype of an AI-driven holographic video communication system and present critical experimental results to evaluate its performance. Finally, we identify future research directions and discuss potential solutions for providing real-time and high-quality holographic experiences. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Multi-Player Immersive Communications and Interactions in Metaverse: Challenges, Architecture, and Future Directions

arXiv

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arXiv 2022年

作者： Huang, Yakun Qiao, Xiuquan Wang, Haowen Su, Xiang Dustdar, Schahram Zhang, Ping State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science Norwegian University of Science and Technology 2815 Gjøvik Norway University of Oulu Oulu90570 Finland Distributed Systems Group Technische Universität Wien Vienna1040 Austria

The metaverse has awakened users’ expectations of an immersive interaction that fuses the virtual digital world and the physical world across space and time. However, the metaverse is still in its infancy, typically expanding multi-player applications (e.g., multi-player games) to implement a prototype with the help of 5G/Beyond 5G, Artificial Intelligence, digital twin, and other enabling technologies. This article reviews the characteristics, key enabling technologies, and driving applications of the state-of-the-art metaverse. We focus on the immersive interactions perspective of the metaverse from the tasks, inputs, and feedback across the users, digital world, and physical world and reveal the key challenges. Afterwards, we present a multi-player interaction prototype platform based on a cloud-edge-device collaborative framework. Also, we evaluate it with centralized and device-to-device (D2D) approaches to verify the efficiency and flexibility of interactions. Finally, we point out future research approaches and discuss potential solutions to enable more stable and higher quality multi-player interactions for metaverse services. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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WaterTS: Integrating Enhanced Transformer, Sliding Block, and Channel Independence for Long-term Water Quality Prediction

WaterTS: Integrating Enhanced Transformer, Sliding Block, an...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Jing Bi Lifeng Xu Ziqi Wang Haitao Yuan Shichao Chen Mu Gu MengChu Zhou Faculty of Information Technology Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and Beijing Huairou Academy of Parallel Sensing Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Nowadays, the deterioration of water resources leads to negative ecological impacts. To effectively inhibit the deterioration of water resources, a water quality prediction model based on enhanced transformer, sliding block, and channel independence (WaterTS) is proposed by comprehensively analyzing the indicators of water resources and making long-term predictions of the dissolved oxygen index. WaterTS adopts a sliding block method to extract the short-term temporal features of the water quality series and combine them with channel independence to make independent predictions of multi-featured data. Moreover, it upgrades the internal encoder structure of the transformer and improves the attention mechanism to Probsparse-attention and Auto-Correlation to speed up the prediction speed. Furthermore, Post LayerNormal is adjusted to Pre LayerNormal, which makes the training gradient more stable and enhances the accuracy of predictions. Experiments are conducted using real-world water environment data, and comparison results with state-of-the-art prediction models show that the WaterTS achieves accurate predictions on both short-term and long-term water quality data.

关键词： Training Accuracy Biological system modeling Time series analysis Water quality Predictive models Transformers Feature extraction Real-time systems Water resources

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BRI-MAC: Broadcast Receiver Initiated MAC protocol for Internet of Things

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computer Communications 2025年

作者： Lakhdar GOUDJIL Samir FENANIR Fouzi SEMCHEDINE Mounir ZERROUG Laboratory of Networks & Distributed Systems Computer Science Department Faculty of Sciences Ferhat Abbas Setif 1 University Algeria Mechatronics Laboratory Optics and Precision Mechanics Institute Ferhat AbbasSetif1 University Algeria Computer Science Department Faculty of Sciences Ferhat Abbas Setif 1 University Algeria

Efficient energy management in IoT networks, particularly at the MAC layer, is essential due to the excessive energy consumption of traditional protocols caused by retransmissions, idle listening, and control overhead. To address these challenges, this paper proposes BRI-MAC (Broadcast Receiver-Initiated MAC), a multi-hop duty cycle MAC protocol that enhances energy efficiency, reduces latency, and optimizes packet delivery. BRI-MAC employs a rendezvous beacon for transmission scheduling and integrates a collision resolution mechanism to minimize energy waste. In addition to performance improvements, BRI-MAC strengthens IoT security by mitigating DDoS, Sybil, and jamming attacks through adaptive scheduling and resilient multi-hop broadcasting. Simulation results demonstrate that BRI-MAC reduces end-to-end delay by 20%, achieves a 97% packet reception rate, maintains low energy consumption (30%), and offers excellent scalability (90%), compared to existing MAC protocols such as RI-MAC and EnRI-MAC. These findings highlight its superiority over conventional MAC protocols, making it a compelling solution for large-scale, energy-constrained IoT deployments.

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An optimal power supervising strategy for a smart home assessed based on BPMN Framework

An optimal power supervising strategy for a smart home asses...

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International Conference on sciences and Techniques of Automatic Control and computer Engineering (STA)

作者： Leila Lanouar Mouna Rekik Olfa Bouchaala Lotfi Krichen Multimédia & Web Department University of Gabes Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Gabes Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax University of Gabes National Engineering School of Sfax (ENIS) Higher Institute of Computer Science and Multimedia of Gabes (ISIMG) Sfax Gabes Tunis Department of Computer Engineering and Applied Mathematics Research Laboratory on Development and Control and Distributed Applications (ReDCAD) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis Electrical Engineering Department Electrical systems and renewable energies Laboratory (LSEER) University of Sfax National Engineering School of Sfax (ENIS) Sfax Tunis

ISBN: (纸本)9781665482622

For each smart home, the need of energy consumption supervision is necessary, which plays an important role to ensure the highest power quality and to enhance the stability of the whole grid. The current document implements a smart home supply strategy based on endless resources to reduce the electricity bill and confirm the energy balance. In this context, a proposed supervision algorithm operates in eight cases to reach optimal energy flow between renewable generators, home battery and grid in a smart home concept is presented. The system is evaluated using the framework “Business Process Model and Notation” (BPMN) Camunda basing on information stored in Firebase Cloud and results are presented in order to manifest the efficiency of this control strategy.

关键词： Renewable energy sources Energy consumption Power quality Process control Smart homes Power system stability Stability analysis

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