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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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IoT-Edge-Cloud computing Framework for QoS-Aware Computation Offloading in Autonomous Mobile Agents: Modeling and Simulation 1

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6th International Conference on Mobile, Secure and Programmable Networking, MSPN 2020

作者： Ismail, Leila Materwala, Huned 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

ISBN: (数字)9783030675509

ISBN: (纸本)9783030675493

Edge-cloud computing is an emerging computational model that allows offloading of service requests by the autonomous mobile agents from the edge-server to the cloud-server. This is to reduce the network latency prevalent in the cloud-IoT model. However, Quality-of-Service (QoS)-Aware computation offloading in a heterogeneous and dynamic edge-cloud environment remains an open problem. In this paper, we propose a queuing theory-based edge-cloud computing framework for QoS-aware offloading in mobile autonomous agents. This framework model decides whether to execute an incoming request to the edge-server on the edge itself or to offload to one of the heterogeneous cloud servers such that the request’s execution time is the minimum. To model a request’s execution time, we consider the processing capabilities and the queues overheads of the edge and cloud servers, and the edge-cloud communications’ time. The details of the evaluation results, using dataset generated from real-life scenarios, are presented in the paper. © 2021, Springer Nature Switzerland AG.

关键词： Quality of service

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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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Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

arXiv

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arXiv 2023年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Liu, Zhiwei Wu, Jia Yu, Zhengtao Yu, Philip S. The Institute of Information Engineering Chinese Academy of Sciences the School of Cyber Security University of Chinese Academy of Sciences Beijing China The School of Cyber Science and Technology Beihang University Beijing1000191 China Salesforce AI Research CA94301 United States The School of Computing Macquarie University Sydney Australia The Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China The Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCLSED, and its variant - UCL-ECSED, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012_100, Events2018_100, and CrisisLexT_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A novel test items evaluation method for mixed-signal system-on-chips based on extra information gain cost

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Measurement 2025年 256卷

作者： Kewei Deng Houjun Wang Pu Pu Taochen Gu Enxiao Liu Zhenyu Zhao School of Automation Engineering University of Electronic Science and Technology of China Chengdu Sichuan China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore A*STAR Institute of High Performance Computing Singapore 138632 Singapore

As system-on-chip (SoC) grow increasingly complex, the number of test items required for accurate characterization continues to rise. However, the high test time necessitates efficient test process optimization. Although several studies have explored in digital chips, where test data are more accessible and scan chains enable efficient defect localization, these methods are often ineffective for analog chips. In mixed-signal SoC tests, advanced optimization algorithms typically rely on the posterior probability knowledge, which is usually unavailable under the stop-on-first-failure mechanism. To overcome these limitations, this paper proposes a novel test items evaluation method by introducing an extra information gain cost (EIGC) index. The proposed method is based on correlation analysis, where each test item is assessed throughout the entire test process without relying on failed chips’ database. It is validated through experiments on the final test and binning process of two mixed-signal SoC designs. Results demonstrate that reordering test sequences and pruning redundant test items based on the EIGC index can achieve a significant reduction in test time without defective chips escaping. To the best of our knowledge, for the first time, such an index is introduced to evaluate the SoC test items under stop-on-first-failure mechanism without requiring posterior probability knowledge.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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引用

2020 International Conference on computer Big Data and Artificial Intelligence, ICCBDAI 2020

作者： Li, Ni Peng, Manman Cao, Buwen Li, Kenli Li, Keqin College of Computer Science and Electric Engineering Hunan University Changsha Hunan410082 China College of Information and Electronic Engineering Yiyang Hunan413000 China Department of Computer Science State University of New York New PaltzNY12561 United States Engineering Research Center of Dong Ting Lake Regional Ecological Environment Intelligent Monitoring Disaster Prevention and Mitigation Hunan Yiyang413000 China Key Laboratory of City Computing and IoT Hunan City UniversityHunan Yiyang413000 China

Spectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI ). © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

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International Conference on Data engineering

作者： Lihui Lin Yanghui Rao Haoran Xie Raymond Y. K. Lau Jian Yin Fu Lee Wang Qing Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Lingnan University New Territories Hong Kong SAR Department of Information Systems City University of Hong Kong Kowloon Hong Kong SAR Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou China School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong SAR

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

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Research on Active Dynamic Trusted Migration Scheme for VM-vTPCM 8th

Research on Active Dynamic Trusted Migration Scheme for VM-v...

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8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： Wang, Xiao Zhang, Jianbiao Zhang, Ai Feng, Xingwei Zeng, Zhiqiang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China Department of Computer Science and Engineering UC San Diego San DiegoCA United States Science and Technology on Information Assurance Laboratory Beijing China

ISBN: (纸本)9789811394089

Dynamic migration of virtual machine (VM) is the main feature of the cloud environment, and trusted computing is one of the core technologies to solve the security problems of the cloud environment. Aiming at the security problem of VM and virtual trusted root (VTR) migration in the cloud environment, an active dynamic trusted migration scheme built upon the Chinese active immune trusted computing (AITC) is investigated in this paper, where migration architecture and protocol are included. To validate the effectiveness of the scheme, experiments are carried out and promising performance is shown in comprehensive experiment results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Trusted computing

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