Article
Computer Science, Information Systems
Long Chen, Xiaoping Li, Yucheng Guo, Ruben Ruiz
Summary: This paper addresses the issue of workflow scheduling with both reserved and on-demand instances in cloud computing, aiming to minimize the total rental cost under deadline constraints through mathematical modeling and optimization algorithms. Experimental results show that the proposed algorithm can achieve considerable cost savings compared to other algorithms.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Harvinder Singh, Sanjay Tyagi, Pardeep Kumar, Sukhpal Singh Gill, Rajkumar Buyya
Summary: This paper discusses various nature-inspired metaheuristic algorithms for scheduling tasks in cloud computing environments and identifies Crow Search Algorithm as the most optimal technique in terms of efficiency and cost through comparative analysis of six algorithms.
SIMULATION MODELLING PRACTICE AND THEORY
(2021)
Article
Computer Science, Information Systems
Hao Yuan, Deke Guo, Hanlong Liao, Rui Wu, Jiangfan Li
Summary: This article proposes a collaborative road detection system that tackles the long response time issue in vehicular cloud computing through task scheduling and uploading strategies.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Mao-Lun Chiang, Hui-Ching Hsieh, Yu-Huei Cheng, Wei-Ling Lin, Bo-Hao Zeng
Summary: Cloud computing has become an ideal way to provide various applications, but efficient task scheduling algorithms are crucial. Existing algorithms lack consideration for load balancing among working nodes, so this paper proposes the BCSV scheduling algorithm, which uses different suffrage values to improve task dispatch performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Yuanjun Laili, Fuqiang Guo, Lei Ren, Xiang Li, Yulin Li, Lin Zhang
Summary: Industrial Internet of Things (IIoT) is becoming intelligent with large-scale collaborative cloud and edge resources, enabling online supervision, fast analysis, and precise control for manufacturing job shops. However, processing large-scale industrial computation online leads to significant communication overhead and energy consumption among cloud, edge, and end devices. To enhance cloud-edge collaboration, this article proposes a practical task scheduling model considering two types of collaborative modes. A parallel group-merge evolutionary algorithm is introduced to assign thousands of tasks within seconds, by dividing them into weakly correlated groups and applying modified evolutionary operators for finding a subsolution for each group. Experimental results demonstrate that this method can achieve swift allocation of tasks to cloud and edge servers, reducing overall task computing time by 36.97% and saving up to 23.71% of energy consumption.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Theory & Methods
Dabiah Alboaneen, Hugo Tianfield, Yan Zhang, Bernardi Pranggono
Summary: This article proposes a new metaheuristic method to optimize joint task scheduling and VM placement in cloud data centers, aiming to achieve better overall results in terms of minimizing execution cost, makespan, and degree imbalance while maximizing resource utilization of physical hosts. Simulation results show that optimizing joint task scheduling and VM placement leads to improved performance compared to traditional task scheduling algorithms.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Mohammad Hasani Zade, N. Mansouri, M. M. Javidi
Summary: Most studies on task scheduling in the cloud focus on a few objectives, but this paper designs a task scheduling problem with conflicting objectives. The proposed algorithm consists of a meta-scheduler and a local-scheduler, and it is evaluated through experiments, demonstrating its performance improvement in waiting time, energy consumption, resource utilization, and security.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Computer Science, Hardware & Architecture
Linhua Ma, Chunshan Xu, Haoyang Ma, Yujie Li, Jiali Wang, Jin Sun
Summary: This paper presents a family of genetic algorithm-based metaheuristics for scheduling tasks in data-intensive BoT applications on hybrid clouds, aiming to minimize the flowtime of applications under a specified budget constraint. By considering communication time and cost, an optimization model is formulated, and an efficient task dispatching method is designed to evaluate the scheduling quality of each chromosome. By incorporating an improved crossover scheme and task dispatching method, the proposed metaheuristic algorithms employ three crossover operators to solve the considered BoT scheduling problem.
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
(2021)
Review
Computer Science, Theory & Methods
Raj Mohan Singh, Lalit Kumar Awasthi, Geeta Sikka
Summary: This study provides a comprehensive taxonomic review and analysis of recent metaheuristic scheduling techniques in cloud and fog environments. It includes evaluation criteria, scheduling objectives, a taxonomy of scheduling algorithms, and rigorous evaluation of existing literature. The study also focuses on the performance of hybrid algorithms.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Samar Hussni Anbarkhan, Mohamed Ali Rakrouki
Summary: This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm to address the issue of high time and cost in scheduling workflow tasks in a cloud computing environment. The algorithm combines intensive tasks to reduce particle dimensions and ensure initial particle quality. It optimizes particle initialization and integrates a self-adaptive function to determine the best direction of the particles. Experimental results show that the proposed enhanced PSO algorithm achieves faster convergence speed and better performance in task execution.
Article
Computer Science, Information Systems
Liyun Zuo, Jieguang He, Yonghui Xu, Lei Zhang
Summary: Compared with cloud computing, edge-cloud collaboration can avoid long transmitting delay to cloud since tasks are close to edge, which makes edge-cloud collaboration suitable for delay-sensitive applications. However, the complex environment of edge-cloud poses new challenge to task scheduling. A Collaborative Scheduling strategy based on task Admission and Delay Evaluation (CSADE) is proposed to deal with the challenge and ensure the quality of service (QoS).
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Biao Hu, Xincheng Yang, Mingguo Zhao
Summary: This paper investigates how to adaptively schedule dynamic applications in a heterogeneous embedded system to minimize energy consumption while meeting response time and reliability requirements. It first studies the scheduling problem for static applications and proposes a reliability-aware strategy. An algorithm is then developed to save static energy consumption by using as few processors as possible. The solution is extended to handle dynamic applications by developing task migration and schedule adjustment algorithms. Extensive simulation experiments demonstrate the high efficiency of the proposed approaches.
JOURNAL OF SYSTEMS ARCHITECTURE
(2023)
Article
Computer Science, Information Systems
An Song, Wei-Neng Chen, Xiaonan Luo, Zhi-Hui Zhan, Jun Zhang
Summary: This article proposes a novel workflow model with composite tasks, which can manage complex workflows and address data transmission between sub-tasks. To solve this problem, a nested particle swarm optimization and a fast version of nested particle swarm optimization are devised.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Computer Science, Hardware & Architecture
P. Rajasekar, Yogesh Palanichamy
Summary: Maximized deployment of workflows in research organizations has led to the emergence of multi-tenant environments that offer workflow deployment as a service. This study proposes a flexible deadline-driven resource provisioning and scheduling algorithm that leverages advanced virtual machine sharing protocol to minimize computing expenses and meet user-assigned deadlines.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Software Engineering
Jinchao Chen, Pengcheng Han, Yifan Liu, Xiaoyan Du
Summary: This article focuses on the scheduling problem of independent tasks in a cloud environment with heterogeneous and distributed resources. An exact formulation based on linear programming is presented to find the optimal allocation schemes for tasks. Inspired by the differential evolution method, a population-based approach is proposed to minimize the total time cost. Experimental results demonstrate the effectiveness and convergence of the proposed approach.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Huifang Li, Jingwei Huang, Mengchu Zhou, Qisong Shi, Qing Fei
Summary: With the development of IoT, self-driving technology has made progress, but safe driving faces challenges in sensing and identifying pedestrian movements from video data. Existing methods fail to capture long-term temporal relationships effectively and do not aggregate discriminative representations properly. To address these issues, this work introduces SP-LTN, an architecture that learns long-term temporal representations and aggregates discriminative representations end-to-end. It also incorporates self-attention pooling, a method to predict the importance scores of representations and highlight their contributions in action recognition. The experimental results demonstrate the superiority of SP-LTN over state-of-the-art methods, using only RGB frames.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Engineering, Industrial
Qi Zhang, Shixin Liu, MengChu Zhou
Summary: This study formulates and investigates a steel grade design problem (SGDP) that arises from the production process of steelmaking continuous casting. For the first time, uncertain yield and demand are considered in the SGDP and a two-stage robust optimization model is constructed accordingly. An enhanced column-and-constraint generation algorithm is proposed to obtain high-quality solutions. The algorithm is tested on extensive instances based on actual production rules and shows effectiveness in solving large-scale SGDPs, outperforming a commonly-used standard algorithm.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Information Systems
Peiyun Zhang, Chenxi Li, Mengchu Zhou, Wenjun Huang, Abdullah Abusorrah, Omaimah O. Bamasag
Summary: This paper proposes a transaction transmission model for blockchain channels based on non-cooperative game theory and presents an optimized channel transaction transmission algorithm. It improves transmission success rate, reduces transmission delay, and effectively decreases transmission overhead by analyzing channel balances, selecting suitable channels, finding Nash equilibrium points, and using iterative sub-gradient methods.
SCIENCE CHINA-INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
ShouGuang Wang, Xin Guo, Oussama Karoui, MengChu Zhou, Dan You, Abdullah Abusorrah
Summary: This study focuses on the deadlock control problem in resource allocation systems using mixed-integer programming and iterative siphon control. It proposes a two-stage deadlock prevention policy, which avoids exhaustive enumeration and reachability analysis.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Review
Biochemistry & Molecular Biology
Shuna Chen, Jiaxin Kang, Huanqing Zhu, Kaixi Wang, Ziyi Han, Leyu Wang, Junsheng Liu, Yuanyuan Wu, Puming He, Youying Tu, Bo Li
Summary: L-theanine is the main amino acid in tea leaves and has both flavor and health benefits. Its immunomodulatory effects, particularly on reducing immunosuppression and improving immunity, have been studied in clinical and epidemiological research. Numerous studies on cells and animals have also shown that theanine plays a role in regulating inflammation, nerve damage, the intestinal tract, and tumors by regulating certain cellular functions.
Review
Biochemistry & Molecular Biology
Hongbo Chen, Fei Yu, Jiaxin Kang, Qiao Li, Hasitha Kalhari Warusawitharana, Bo Li
Summary: The composition and contents of organic acids vary in different types of tea. They play important roles in the metabolism of tea plants, nutrient absorption and growth regulation, as well as the aroma and taste quality of tea. However, the research on organic acids in tea is still limited. This article reviewed the progress made in studying organic acids in tea, including analysis methods, physiological functions, composition and influencing factors, sensory contributions, as well as health benefits like antioxidation and digestion promotion. It aims to provide references for further research on organic acids in tea.
Article
Plant Sciences
Yuanfei Pan, Mu Liu, Alejandro Sosa, Bo Li, Mang Shi, Xiaoyun Pan
Summary: This study investigates the metacommunities of endophytic fungi in the leaves of an invasive plant and finds that the structure of these fungal communities is influenced by multiple spatial scales and different drivers. These findings are important for understanding the global patterns of fungal diversity.
Article
Automation & Control Systems
Huan Liu, Junqi Zhang, MengChu Zhou
Summary: This paper proposes an adaptive particle swarm optimizer that combines hierarchical learning with variable population to enhance the performance of the PSO algorithm. By introducing a heap-based hierarchy and adjusting the particle's level based on its current fitness, as well as eliminating redundant particles based on the population's evolution state, the swarm's exploratory and exploitative capabilities are improved.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Weiling Li, Renfang Wang, Xin Luo, MengChu Zhou
Summary: This study proposes a second-order symmetric non-negative latent factor model (SNLF)-N-2 with an efficient second-order learning algorithm for precise representation of undirected weighted networks. The model applies a single latent factor-related mapping function to achieve an unconstrained learning objective and optimizes this objective with a second-order learning algorithm. Empirical studies show that the proposed model outperforms existing second-order SNLF models in accurately representing real-world undirected weighted networks.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Leifeng He, Guanjun Liu, Mengchu Zhou
Summary: This article presents a method to address privacy issues in multiagent systems using reduced ordered binary decision diagrams (ROBDD). By designing related algorithms and a model checking tool, complex CTLK formulas can be efficiently verified.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Yu Xie, Guanjun Liu, Chungang Yan, Changjun Jiang, MengChu Zhou
Summary: This study proposes a new model to extract transactional behaviors of credit card users and learn new transactional behavioral representations for fraud detection. The model utilizes time-aware gates and an attention module to capture long- and short-term transactional habits of users and extract behavioral motive and periodicity from historical transactions.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Guanyu Cai, Lianghua He, MengChu Zhou, Hesham Alhumade, Die Hu
Summary: This article explores the performance of adversarial-training-based unsupervised domain adaptation (UDA) methods with Lipschitz constraints when dealing with complex source and target datasets with large distribution discrepancies. The connection between Lipschitz constraints and the error bound of UDA is analyzed, demonstrating how Lipschitzness reduces the error bound. Experimental results show that considering the sample amount of the target domain, dimension, and batch size is crucial for the effectiveness and stability of UDA. The model performs well on standard benchmarks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
ChengRan Lin, ZhengCai Cao, MengChu Zhou
Summary: This study addresses the extended version of the flexible job-shop problem and proposes a learning-based cuckoo search algorithm to obtain reliable and high-quality schedules. By introducing a sparse autoencoder and a factorization machine, the algorithm achieves promising results. Numerical simulations show that it outperforms traditional methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Ziqian Wang, Shangce Gao, Mengchu Zhou, Syuhei Sato, Jiujun Cheng, Jiahai Wang
Summary: Feature selection is a multiobjective optimization problem that aims to reduce the number of selected features and improve classification performance. This article proposes an Information-theory-based Nondominated Sorting ACO (INSA) method to tackle the problematic characteristics in feature selection. Experimental results demonstrate that INSA is capable of obtaining feature subsets with better performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Chemical
Ren Wang, Mengchu Zhou, Jinglin Wang, Kaizhou Gao
Summary: Shortest path problems are common in various engineering applications, and the dynamic changes in the environment make it challenging for traditional methods to meet real-time requirements. This paper proposes an improved discrete Jaya algorithm (IDJaya) with a local search operation to explore solutions and improve quality. Experimental results on real road networks demonstrate the superiority of IDJaya over other algorithms, making it suitable for real-time applications.
