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  • Open Access


    Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

    Shasha Zhao1,2,3,*, Huanwen Yan1,2, Qifeng Lin1,2, Xiangnan Feng1,2, He Chen1,2, Dengyin Zhang1,2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1135-1156, 2024, DOI:10.32604/cmc.2024.045660

    Abstract Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment. Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios. In this work, the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm (HPSO-EABC) has been proposed, which hybrids our presented Evolutionary Artificial Bee Colony (EABC), and Hierarchical Particle Swarm Optimization (HPSO) algorithm. The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm. Comprehensive testing including evaluations of algorithm convergence speed, resource execution time, load balancing,… More >

  • Open Access


    Distributed Active Partial Label Learning

    Zhen Xu1,2, Weibin Chen1,2,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2627-2650, 2023, DOI:10.32604/iasc.2023.040497

    Abstract Active learning (AL) trains a high-precision predictor model from small numbers of labeled data by iteratively annotating the most valuable data sample from an unlabeled data pool with a class label throughout the learning process. However, most current AL methods start with the premise that the labels queried at AL rounds must be free of ambiguity, which may be unrealistic in some real-world applications where only a set of candidate labels can be obtained for selected data. Besides, most of the existing AL algorithms only consider the case of centralized processing, which necessitates gathering together all the unlabeled data in… More >

  • Open Access


    Edge Intelligence with Distributed Processing of DNNs: A Survey

    Sizhe Tang1, Mengmeng Cui1,*, Lianyong Qi2, Xiaolong Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 5-42, 2023, DOI:10.32604/cmes.2023.023684

    Abstract With the rapid development of deep learning, the size of data sets and deep neural networks (DNNs) models are also booming. As a result, the intolerable long time for models’ training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually. Moreover, devices stay idle in the scenario of edge computing (EC), which presents a waste of resources since they can share the pressure of the busy devices but they do not. To address the problem, the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of… More >

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