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Hierarchical three-way decision fusion for multigranularity GPU-CPU coscheduling in hybrid computing systems 为混合计算系统中的多粒度 GPU-CPU 协同调度提供分层三向决策融合

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-06 DOI: 10.1016/j.ins.2025.122048

Chunmao Jiang, Yongpeng Wang

{"title":"Hierarchical three-way decision fusion for multigranularity GPU-CPU coscheduling in hybrid computing systems","authors":"Chunmao Jiang, Yongpeng Wang","doi":"10.1016/j.ins.2025.122048","DOIUrl":"10.1016/j.ins.2025.122048","url":null,"abstract":"<div><div>In heterogeneous computing environments, coscheduling of the graphics processing unit (GPU) and central processing unit (CPU) poses substantial challenges because of the diverse hardware architectures and dynamic workload patterns. To address this, we propose a novel hierarchical three-way decision fusion (H3WDF) strategy that integrates multigranularity workload predictions and adaptive scheduling policies. H3WDF employs a three-tier decision-making process, achieving global coordination through selective aggregation of localized decisions while establishing a balance between efficiency and quality of service. Results of experimental evaluation in a heterogeneous environment comprising several GPUs demonstrate the superior performance of H3WDF across multiple metrics. For “large language model” workloads, H3WDF achieves remarkable prediction accuracy both for short- and long-term forecasts. H3WDF's three-way decision mechanism effectively distributes workloads, balancing between batched executions for training tasks and immediate executions for inference workloads. Resource utilization exhibits significant improvements across all GPU types, with particularly strong performance in the case of high-end GPUs. Compared with the state-of-the-art baselines, H3WDF substantially reduces job completion times, enhances energy efficiency, and consistently maintains high fairness in resource allocation.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122048"},"PeriodicalIF":8.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable service recommendation for interactive mashup development counteracting biases

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-06 DOI: 10.1016/j.ins.2025.122049

Yueshen Xu , Shaoyuan Zhang , Honghao Gao , Yuyu Yin , Jingzhao Hu , Rui Li

{"title":"Explainable service recommendation for interactive mashup development counteracting biases","authors":"Yueshen Xu , Shaoyuan Zhang , Honghao Gao , Yuyu Yin , Jingzhao Hu , Rui Li","doi":"10.1016/j.ins.2025.122049","DOIUrl":"10.1016/j.ins.2025.122049","url":null,"abstract":"<div><div>Web services have been prevalently applied in many software development scenarios such as the development of many applications in the cloud, mobile networks, and Web. But Web services usually suffer from the serious issue of single functionality; thus in recent years, compositions of Web services, i.e., mashups, have become a popular choice, and have brought significant convenience in providing more comprehensive functionalities. But the diversity and number of Web services are expanding dramatically, resulting in an intractable challenge: how to effectively recommend Web services for mashup development. Researchers have proposed several recommendation approaches, but existing solutions are primarily applicable in a one-shot paradigm, which may introduce biases and usually lack explainability. In real-world scenarios, developers usually need to incorporate new Web services to address emerging challenges, implying that the development paradigm could be interactive. Moreover, existing approaches are prone to produce mediocre accuracy. To solve these issues, in this paper, we develop an innovative <em>Multimodal Features-based Unbiased (MMFU)</em> service recommendation framework for interactive mashup development, which takes full advantage of the multimodal features involved in the development procedure. Our MMFU framework encompasses two separate models developed to learn deep features from both text and graph structural information, and contains a feature fusion mechanism. Extensive experiments were performed on two real-world datasets, and the results revealed that the MMFU framework outperforms the compared existing state-of-the-art approaches, and has high explainability and the ability to counteract biases.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122049"},"PeriodicalIF":8.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multiple level competitive swarm optimizer based on dual evaluation criteria and global optimization for large-scale optimization problem

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122068

Chen Huang , Yingjie Song , Hongjiang Ma , Xiangbing Zhou , Wu Deng

{"title":"A multiple level competitive swarm optimizer based on dual evaluation criteria and global optimization for large-scale optimization problem","authors":"Chen Huang , Yingjie Song , Hongjiang Ma , Xiangbing Zhou , Wu Deng","doi":"10.1016/j.ins.2025.122068","DOIUrl":"10.1016/j.ins.2025.122068","url":null,"abstract":"<div><div>Large-scale optimization problems (LSOPs) in science and technology bring great challenges to the performance of algorithms. Although Competitive swarm optimizer (CSO) is an effective method, some shortcomings still exist when handling LSOPs, such as premature convergence. Therefore, a novel multiple level CSO with dual evaluation criteria and global optimization (DEGMCSO) is proposed to seek optimal solutions of LSOPs. In this paper, dual evaluation criteria are inserted into the multiple comparison process of the losers and winners to assist the algorithm retain more high quality particles with the potential. In addition to fitness values, adaptive selection weight fitness-distance is designed as the other criterion for selecting winners and losers according to different optimization problems. Meanwhile, a simple global optimal modification strategy is employed to get high quality global best solution. By CEC2010 and CEC2013 function suits, the results indicate DEGMCSO outperforms some popular algorithms. Finally, DEGMCSO is applied to feather selection problems of high dimension classification in the real world. The simulation results show that compared with the original CSO algorithm, DEGMCSO can find the solution of 16 functions on CEC2010 test function set which is obviously better than the CSO algorithm.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122068"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-view ordinal regression with feature augmentation and privileged information learning

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122065

Yanshan Xiao , Linbin Chen , Bo Liu

{"title":"Multi-view ordinal regression with feature augmentation and privileged information learning","authors":"Yanshan Xiao , Linbin Chen , Bo Liu","doi":"10.1016/j.ins.2025.122065","DOIUrl":"10.1016/j.ins.2025.122065","url":null,"abstract":"<div><div>Ordinal regression deals with the classification problems that the classes are ranked in order. The majority of existing ordinal regression approaches are designed for single-view data, and only a little work is done on multi-view ordinal regression. However, these multi-view ordinal regression works mainly concentrate on the consensus information between different views, while the complementary information that is critical in multi-view learning is not adequately considered in learning the ordinal regression classifier. In this paper, we put forward the multi-view ordinal regression model that incorporates feature augmentation and privileged information learning (MORFP). Firstly, distinguished from the existing multi-view ordinal regression approaches that mainly embody the consensus principle, MORFP introduces the concept of privileged information learning and implements both the consensus and complementarity principles. Based on the concept of privileged information learning, we treat one view as the privileged information of another view, so that different views can supply complementary information to enhance each other. Secondly, considering that the distributions of data in distinct views may be are greatly different, we map those views to a common subspace and augment this subspace by incorporating the original features of each view. By combining the original features in the views and projected features in the common subspace, the learned ordinal regression classifier is expected to have better discriminative ability than that learned on only the projected features or the original features. Lastly, we employ a heuristic framework to resolve the learning problem of MORFP, which trains the multi-view ordinal regression classifier and optimizes the projection matrices alternately. Numerical studies on real-life datasets have demonstrated that MORFP performs explicitly better than the existing multi-view ordinal regression approaches.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122065"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fixed-time control of multi-motor nonlinear systems via adaptive neural network dual sliding mode

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122061

Wanjun Jing , Meng Li , Yong Chen , Zhangyong Chen

引用次数: 0

Granular ball-based partial label feature selection via fuzzy correlation and redundancy

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122047

Wenbin Qian , Junqi Li , Xinxin Cai , Jintao Huang , Weiping Ding

{"title":"Granular ball-based partial label feature selection via fuzzy correlation and redundancy","authors":"Wenbin Qian , Junqi Li , Xinxin Cai , Jintao Huang , Weiping Ding","doi":"10.1016/j.ins.2025.122047","DOIUrl":"10.1016/j.ins.2025.122047","url":null,"abstract":"<div><div>Partial label learning is a weakly supervised framework in which each training sample is associated with a set of candidate labels, but only one among them is the true label. Feature selection is a technique for enhancing the ability of learning models to generalize effectively. However, a challenging problem in feature selection for partial label learning is the impact of ambiguous candidate labels. To address this, this paper proposes a granular ball-based partial label feature selection method via fuzzy correlation and redundancy. Firstly, the paper utilizes granular ball computing to obtain two granular ball sets that respectively reflect the supervision information from candidate and non-candidate labels. The relative density between two granular ball sets is used to obtain labeling confidence which can identify the ground-truth labels. Then, a novel fuzzy entropy is defined by combining consistency in the granular ball with fuzzy information entropy. Additionally, fuzzy mutual information is derived by considering the fuzzy entropy and the fuzzy similarity constrained by granular ball radius. Fuzzy correlation and redundancy is measured by granular ball-based fuzzy mutual information. A heuristic search strategy is used to rank the features according to the principle of maximizing relevance and minimizing redundancy. Finally, experimental results on five real-world datasets and eight controlled UCI datasets show that the proposed method obtains superior performance than other compared methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"709 ","pages":"Article 122047"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-class graph autoencoder: A new end-to-end, low-dimensional, and interpretable approach for node classification

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122060

Marcos Paulo Silva Gôlo, José Gilberto Barbosa de Medeiros Junior, Diego Furtado Silva, Ricardo Marcondes Marcacini

{"title":"One-class graph autoencoder: A new end-to-end, low-dimensional, and interpretable approach for node classification","authors":"Marcos Paulo Silva Gôlo, José Gilberto Barbosa de Medeiros Junior, Diego Furtado Silva, Ricardo Marcondes Marcacini","doi":"10.1016/j.ins.2025.122060","DOIUrl":"10.1016/j.ins.2025.122060","url":null,"abstract":"<div><div>One-class learning (OCL) for graph neural networks (GNNs) comprises a set of techniques applied when real-world problems are modeled through graphs and have a single class of interest. These methods may employ a two-step strategy: first representing the graph and then classifying its nodes. End-to-end methods learn the node representations while classifying the nodes in OCL process. We highlight three main gaps in this literature: (i) non-customized representations for OCL; (ii) the lack of constraints on hypersphere learning; and (iii) the lack of interpretability. This paper presents <strong><u>O</u></strong>ne-c<strong><u>L</u></strong>ass <strong><u>G</u></strong>raph <strong><u>A</u></strong>utoencoder (OLGA), a new OCL for GNN approach. OLGA is an end-to-end method that learns low-dimensional representations for nodes while encapsulating interest nodes through a proposed and new hypersphere loss function. Furthermore, OLGA combines this new hypersphere loss with the graph autoencoder reconstruction loss to improve model learning. The reconstruction loss is a constraint to the sole use of the hypersphere loss that can bias the model to encapsulate all nodes. Finally, our low-dimensional representation makes the OLGA interpretable since we can visualize the representation learning at each epoch. OLGA achieved state-of-the-art results and outperformed six other methods with statistical significance while maintaining the learning process interpretability with its low-dimensional representations.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122060"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consensus control for multi-agent systems with unknown faded neighborhood information via iterative learning scheme

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122050

Wanzheng Qiu , JinRong Wang , Dong Shen

{"title":"Consensus control for multi-agent systems with unknown faded neighborhood information via iterative learning scheme","authors":"Wanzheng Qiu , JinRong Wang , Dong Shen","doi":"10.1016/j.ins.2025.122050","DOIUrl":"10.1016/j.ins.2025.122050","url":null,"abstract":"<div><div>In this paper, the consensus problem of multi-agent systems with unknown faded neighborhood information is addressed using the iterative learning control method. Considering that information exchange in wireless networks may be disturbed by unknown fading effects and unknown additive noise, it is significant to realize accurate consensus tracking of each agent to a given leader under the contaminated information. Unlike the traditional mechanism of correcting unknown faded neighborhood information by estimating the statistical characteristics of random fading variables, we introduce test signals to correct the trajectory signals of each agent. As no estimation mechanism is involved, the storage and computational burden of the whole system are greatly reduced. Based on a classic distributed structure and a novel correction mechanism, two novel distributed learning consensus control schemes are constructed. The consensus results of multi-agent systems under the two learning control schemes are discussed in detail using mathematical analysis tools. Finally, the multi-pendulum network system is simulated to verify the theoretical results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122050"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability-driven joint clustering based on hybrid attribute analysis for supporting social network large-scale decision-making 基于混合属性分析的可靠性驱动联合聚类，支持社交网络大规模决策

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122062

Sumin Yu , Jia Xiao , Zhijiao Du , Xuanhua Xu

{"title":"Reliability-driven joint clustering based on hybrid attribute analysis for supporting social network large-scale decision-making","authors":"Sumin Yu , Jia Xiao , Zhijiao Du , Xuanhua Xu","doi":"10.1016/j.ins.2025.122062","DOIUrl":"10.1016/j.ins.2025.122062","url":null,"abstract":"<div><div>Social network large-scale decision-making (SNLSDM) has become an important domain in the field of decision science. A major challenge in solving such problems lies in the effective data dimensionality reduction through clustering techniques. While the reliability of evaluation information significantly influences clustering quality, most clustering algorithms overlook this critical factor. To address this gap, this article proposes novel reliability-driven joint clustering algorithms based on hybrid attribute analysis for SNLSDM problems. First, the probabilistic linguistic evaluation-reliability function (PL-ERF) is defined to handle fuzzy evaluations and social networks, along with its operational rules. We describe the configuration of SNLSDM with PL-ERFs. Subsequently, the hybrid attribute analysis is conducted to process the initial trust social network. A new reliability-based trust propagation method is designed to construct a complete trust social network. By combining trust and similarity information, a compatibility network is established. Accordingly, we develop reliability-driven joint clustering algorithms that consider multiple constraints, including similarity, trust, and compatibility. We also discuss the time complexity analysis and scalability of the algorithms. Finally, a numerical experiment and two real-word cases studies illustrate the feasibility and effectiveness of the algorithms. A comparative analysis highlights the impact and advantages of incorporating reliability into clustering.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"709 ","pages":"Article 122062"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-way decision-based reinforcement learning for container vertical scaling

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122045

Chunmao Jiang , Guojun Mao , Bin Xie

{"title":"Three-way decision-based reinforcement learning for container vertical scaling","authors":"Chunmao Jiang , Guojun Mao , Bin Xie","doi":"10.1016/j.ins.2025.122045","DOIUrl":"10.1016/j.ins.2025.122045","url":null,"abstract":"<div><div>Container-based cloud computing requires efficient and adaptive resource management, particularly when making vertical scaling decisions. Traditional approaches often struggle with workload variability and lack flexibility when faced with uncertainties in workload patterns. This paper introduces a novel three-way decision-based reinforcement learning (TWD-RL) model for container vertical scaling. The TWD-RL model partitions the state space into positive, boundary, and negative regions based on confidence measures derived from historical data and current system states. This partitioning enables more nuanced scaling decisions: immediate scaling in high-confidence states, deferring decisions in uncertain states, and exploring in low-confidence states. We provide a theoretical analysis of the model's convergence properties and optimality conditions, thus establishing its mathematical foundation. Furthermore, we evaluate our model using real-world workload data from the Google Cloud Platform. The results demonstrate that TWD-RL significantly outperforms traditional Vertical Pod Autoscaler (VPA) approaches with respect to average response time, Service Level Agreement (SLA) violations, and resource utilization efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122045"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0