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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

Journal of computer science & technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization

arXiv

引用

arXiv 2023年

作者： Guo, Yanheng Zhang, Yan Wu, Linjie Li, Mengxia Cai, Xingjuan Chen, Jinjun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan030024 China State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in service deployment and task offloading while balancing multiple objectives. This study investigates service deployment and task offloading challenges in a multi-user environment, framing them as a multi-task high-dimensional multi-objective optimization (MT-HD-MOO) problem within an edge environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost as deployment objectives, network reliability is also incorporated. Furthermore, to promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective, in addition to latency, energy consumption, and cost. Additionally, this paper designs a MT-HD-MOO algorithm based on a multi-selection strategy to address this model and its solution. By employing diverse selection strategies, an environment selection strategy pool is established to enhance population diversity within the high-dimensional objective space. Ultimately, the algorithm’s effectiveness is verified through simulation experiments. © 2023, CC BY-NC-ND.

关键词： Multiobjective optimization

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Modeling and Path Generation Approaches for Crowd Simulation Based on Computational Intelligence

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Chinese Journal of Electronics 2023年第4期21卷 636-641页

作者： Hong LIU Yuling SUN Yuanyuan LI School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

Modeling and simulating group behaviours have been an active research topic in the field of computer animation and game. This paper presents some novel approaches for supporting entity modeling and path generation in crowd simulation. It analyses related work about crowd simulation first. Then, an entity modeling approach based on CGA (Cellular genetic algorithm) and NURBS (Non uniform relational B splines) technologies is presented. Next, following the analysis to PSO (Particle swarm optimization) and ABC (Artificial bee colony) algorithms, a crowd path generative approach based on ABC- PSO is put forward. After that, a simulating example of crowd cohesion and performance comparison are exhibited for showing the efficiency of the algorithms. Finally, the current work is summarized and an outlook for the future work is given.

关键词： Surface reconstruction Computational modeling Sociology Animation Classification algorithms Surface topography Splines (mathematics)

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

引用

IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Tao Huang Xiang Chen Li Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China Hunan Key Laboratory for Service computing and Novel Software Technology

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis.

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La-substituted W-type barium-nickel ferrites for tunable and high-performance electromagnetic wave absorption

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International Journal of Minerals,Metallurgy and Materials 2025年第3期32卷 645-656页

作者： Long Wang Jiurong Liu Shenghui Xie Yanli Deng Zhou Wang Shanyue Hou Shengying Yue Gang Wang Na Wu Zhihui Zeng Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials(Ministry of Education) School of Materials Science and EngineeringShandong UniversityJinan 250061China Electronic Materials Research Laboratory Key Laboratory of the Ministry of Education and International Center for Dielectric ResearchSchool of Electronic Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China Army Engineering University of PLA College of Field Engineering Nanjing 210007China Laboratory for Multiscale Mechanics and Medical Science State Key Laboratory for Strength and Vibration of Mechanical StructuresSchool of AerospaceXi’an Jiaotong UniversityXi’an 710049China Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbana 61801USA School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100China

W-type barium-nickel ferrite(BaNi_(2)Fe_(16)O_(27))is a highly promising material for electromagnetic wave(EMW)absorption be-cause of its magnetic loss capability for EMW,low cost,large-scale production potential,high-temperature resistance,and excellent chemical ***,the poor dielectric loss of magnetic ferrites hampers their utilization,hindering enhancement in their EMW-absorption *** efficient strategies that improve the EMW-absorption performance of ferrite is highly desired but re-mains ***,an efficient strategy substituting Ba^(2+)with rare earth La^(3+)in W-type ferrite was proposed for the preparation of novel La-substituted ferrites(Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)).The influences of La^(3+)substitution on ferrites’EMW-absorption performance and the dissipative mechanism toward EMW were systematically explored and ***^(3+)efficiently induced lattice defects,enhanced defect-induced polarization,and slightly reduced the ferrites’bandgap,enhancing the dielectric properties of the ***^(3+)also enhanced the ferromagnetic resonance loss and strengthened magnetic *** effects considerably improved the EMW-absorption perform-ance of Ba_(1-x)LaxNi_(2)Fe_(15.4)O_(27)compared with pure W-type *** x=0.2,the best EMW-absorption performance was achieved with a minimum reflection loss of-55.6 dB and effective absorption bandwidth(EAB)of 3.44 GHz.

关键词： electromagnetic wave absorption W-type barium ferrite rare earth dielectric

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MoMa: A Modular Deep Learning Framework for Material Property Prediction

arXiv

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arXiv 2025年

作者： Wang, Botian Ouyang, Yawen Li, Yaohui Wang, Yiqun Cui, Haorui Zhang, Jianbing Wang, Xiaonan Ma, Wei-Ying Zhou, Hao Tsinghua University China Department of Computer Science and Technology Tsinghua University China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University China Department of Chemical Engineering Tsinghua University China

Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a Modular framework for Materials that first trains specialized modules across a wide range of tasks and then adaptively composes synergistic modules tailored to each downstream scenario. Evaluation across 17 datasets demonstrates the superiority of MoMa, with a substantial 14% average improvement over the strongest baseline. Few-shot and continual learning experiments further highlight MoMa’s potential for real-world applications. Pioneering a new paradigm of modular material learning, MoMa will be open-sourced to foster broader community collaboration. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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scIAMC:Single-Cell Imputation via adaptive matrix completion

scIAMC:Single-Cell Imputation via adaptive matrix completion

引用

IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Shuai Zhang Xiang Chen Li Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China Hunan Key Laboratory for Service computing and Novel Software Technology

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts.

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Response

引用

Gastrointestinal endoscopy 2024年第3期99卷 476-477页

作者： Xiang Zhang Dehua Tang Jin-Dong Zhou Wu-Jun Li Lei Wang Department of Gastroenterology Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Nanjing China. National Institute of Healthcare Data Science at Nanjing University Nanjing China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China. National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Center for Medical Big Data Nanjing Drum Tower Hospital Affiliated Drum Tower Hospital Medical School of Nanjing University Nanjing China.

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An Improved Scheduling Algorithm Based on Integer Programming in Grid Computing

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Chinese Journal of Electronics 2023年第2期18卷 307-311页

作者： Kan Yi Ruchuan Wang Yimu Ji College of Computer Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Task scheduling is one of key issues in grid computing. This paper focused on the task scheduling problem with a large scale of independent and identical tasks. An improved task scheduling algorithm DMIP is put forward, which is based on time and cost constrains combined with Integer programming but can control the max number of tasks dynamically. Compared with plain Integer programming algorithm, named IP, which only considering time constrains, DMIP algorithm reduced both loss ratios of tasks during the submitting process and the total execution cost by simulation experiments.

关键词： Integer programming Costs Scheduling algorithms Heuristic algorithms Computational modeling Time factors IP networks

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Distributed Data Mining Based on Grid Services Pool

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Chinese Journal of Electronics 2023年第2期18卷 220-224页

作者： Song Deng Ruchuan Wang Minghui Yang School of Computer Nanjing University Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

This paper presents a Distributed data mining platform on Grid services pool (DDM-GSP), which combines grid services pool with distributed data mining to solve problems of traditional distributed data mining. Meanwhile, this paper implements parallel distributed genetic algorithm to resolve complex function optimization on basis of DDM-GSP. Simulation experiments show that for concentrative mining, with the augmentation of population sizes, the convergent speed of standard genetic algorithm is improved by 39 times and computing time is improved by 81 times. However, improvement of population dimensions, the average consumptive time of distributed genetic algorithm on grid is about 31.7% less than standard concentrative genetic algorithm, while about 28.6% by contrast to traditional parallel genetic algorithm.

关键词： Computational modeling Sociology Education Data mining Statistics Optimization Standards

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