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18th International Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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SmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum

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Future Generation Computer Systems 2025年 171卷

作者： Aznar Poveda, Juan Ebner, Maximilian Franz Fahringer, Thomas Najafabadi Samani, Zahra Etheredge, Marlon Pedratscher, Stefan Saurabh, Nishant Distributed and Parallel Systems Group University of Innsbruck Technikerstraße 21a Innsbruck6020 Austria Department of Information and Computing Sciences Utrecht University Princetonplein 5 Utrecht3584 Netherlands

Many data-intensive and distributed applications rely on low-latency and scalable key–value storage systems across the computing Continuum. Key–value storage systems typically use consistent hashing or hash slot-sharding mechanisms to distribute data across storage nodes, which ensures load balancing but often leads to sub-optimal response times and monetary costs, particularly in geo-distributed systems where nodes might have different unit prices and be widely dispersed. In this paper, we propose SmartKV, a cost-efficient geo-distributed key–value store that optimizes data placement dynamically, abstracting the intricacies of data organization, transfer, access, and processing. SmartKV integrates a decentralized data placement algorithm that optimizes the replication factor and selects suitable locations for key–value pairs and replicas, balancing cost and access latency while keeping optimization overhead low. We employ a realistic cost model based on public and private Cloud and Edge providers that consider data transfer, request, and storage costs. In addition to conventional key–value pairs, SmartKV supports active key–value pairs, which enable the definition of custom data types and the execution of user-defined functions directly on the storage side. This contributes to reducing data transfer costs and round-trip times. We thoroughly evaluate SmartKV across different regions of the Chameleon testbed using several realistic workloads. Results show that the utilized decentralized data placement strategy allows SmartKV to reduce round trip times between 9 and 84% while reducing costs up to 4.84× under different client workloads and consistency models compared to state-of-the-art data placement strategies. © 2025 The Authors

关键词： Cloud storage

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HyperGAC: Information Entropy-Driven Resource Allocation for Cloud-Edge-End computing

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Journal of Grid computing 2025年第2期23卷 1-23页

作者： Chen, Juan Bai, Lin Chen, Ningjiang Yang, Yubin School of Computer and Electronic Information Guangxi University Nanning China Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Education Department of Guangxi Zhuang Autonomous Region Nanning China Guangxi Center of Technology Innovation for Intelligent Digital Services Nanning China

Cloud-Edge-End computing provides favorable support for emerging applications by virtue of its low-latency and high reliability. Resource allocation strategies significantly impact the quality of service and response time of cloud-edge-end systems. Due to the decentralized storage of resources, large fluctuations in task demands, and the complexity of cloud-edge-end environments, achieving effective resource allocation remains challenging. Existing resource allocation strategies rely on static models or predefined rules, and cannot flexibly respond to real-time network conditions and changes in task requirements. Consequently, they are limited in achieving optimal resource utilization and effective load balancing. Therefore, this paper proposes an Information Entropy (IE)-driven cloud-edge-end resource allocation mechanism called HyperGAC. First, IE is employed to quantify resource fluctuations, so as to accurately capture the dynamic changes in resource demands. And an Entropy-based Correlation Recurrent Unit (ECRU) is designed to predict server resource utilization. Second, the hypergraph is used to model the complex relationships between resources and tasks, breaking through the limitations of traditional graph structures in multi-dimensional relationships modeling. Finally, the Hypergraph Neural Network (HGNN) is integrated into the actor-critic framework to extract the features from the hypergraph association model, while dynamically optimizing resource allocation strategy based on the prediction results. Experimental results demonstrate that the HyperGAC mechanism significantly improves resource utilization and load balancing performance while effectively reducing the task rejection rate.

关键词： Cloud-edge-end computing Deep reinforcement learning Information entropy Resource allocation Resource utilization prediction

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An End-to-End Classification Network Model Based on Hybrid Supervision for Industrial Surface Defect Detection

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International Journal of Pattern Recognition and Artificial Intelligence 2025年第5期39卷

作者： Qin, Runbing Chen, Ningjiang Gan, Shukun Guangxi University School of Computer Electronics and Information Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning530004 China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning530004 China

Industrial part surface defect detection aims to precisely locate defects in images, which is crucial for quality control in manufacturing. The traditional method needs to be designed in advance, but it has shortcomings in terms of generalization ability. Deep neural network-based defect detection faces issues such as low resolution, data scarcity, labeling costs, and high computation. Therefore, an improved solution is necessary to enhance industrial product quality control efficiency and accuracy. A new method is proposed that utilizes end-to-end training of a two-stage neural network based on segmentation with an extended training process. The gradient flow from the classification to the segmentation network is adjusted to prevent unstable features from interfering with learning. To address the problem of image oversampling and undersampling during training, a frequency sampling scheme for negative samples is introduced. Additionally, positive pixels in the region-based segmentation mask are weighted using the distance transform algorithm so that regions with a high probability of defects can be detected without detailed annotation. Experiments are conducted across three distinct defect datasets by applying hybrid supervision encompassing diverse conditions. In the optimal case, the AP rate of the proposed model on the DAGM and KolektorSDD datasets reaches 100%, and the AP rate on the Severstal Steel dataset reaches 98.99%. The experiments’ results show that the detection accuracy has improved greatly, showing how well the proposed method works in the industry. © 2025 World Scientific Publishing Company.

关键词： Deep neural networks

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Auxo: an architecture-centric framework supporting the online tuning of software adaptivity

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Science China(Information Sciences) 2015年第9期58卷 31-45页

作者： WANG HuaiMin DING Bo SHI DianXi CAO JianNong Alvin T.S.Chan National Key Laboratory of Parallel and Distributed Processing College of ComputerNational University of Defense Technology Department of Computing Hong Kong Polytechnic University

Adaptivity is the capacity of software to adjust itself to changes in its environment. A common approach to achieving adaptivity is to introduce dedicated code during software development stage. However,since those code fragments are designed a priori, self-adaptive software cannot handle situations adequately when the contextual changes go beyond those that are originally anticipated. In this case, the original builtin adaptivity should be tuned. For example, new code should be added to provide the capacity to sense the unexpected environment or to replace outdated adaptation decision logic. The technical challenges in this process, especially that of tuning software adaptivity at runtime, cannot be understated. In this paper,we propose an architecture-centric application framework for self-adaptive software named Auxo. Similar to existing work, our framework supports the development and running of self-adaptive software. Furthermore,our framework supports the tuning of software adaptivity without requiring the running self-adaptive software to be terminated. In short, the architecture style that we are introducing can encapsulate not only general functional logic but also the concerns in the self-adaptation loop(such as sensing, decision, and execution)as architecture elements. As a result, a third party, potentially the operator or an augmented software entity equipped with explicit domain knowledge, is able to dynamically and flexibly adjust the self-adaptation concerns through modifying the runtime software architecture. To truly exercise, validate, and evaluate our approach,we describe a self-adaptive application that was deployed on the framework, and conducted several experiments involving self-adaptation and the online tuning of software adaptivity.

关键词： software architecture self-adaptive software architecture style application framework software adaptation

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On Automated Testing of Description Logic Reasoners

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5th International Conference on distributed computing and Internet Technologies (ICDCIT)

作者： Babik, Marian Hluchy, Ladislav Department of Parallel and Distributed Computing Institute of Informatics Slovak Academy of Sciences Slovakia

ISBN: (纸本)9783540897361

OWL and RDF/RDFS are ontological languages developed by the World Wide Web Consortium (W3), which have become a de facto standard for the, ontological descriptions in various domains. The evolution of these standards was influenced by the numerous advances in the research of knowledge representation and reasoning. Although support for reasoning and standardized representation is the key benefit, of these technologies, there is a lack of existing test, frameworks, which would be capable of addressing many crucial aspects of the Semantic Web applications. In this paper we propose a methodology for automated testing of OWL reasoners based on the real-world ontologies. This specification covers both terminological and assertional reasoning as well as checking of the correctness of the answers. An open-source implementation of such framework is described and a study of initial result is provided. The tests cover an extensive set of reasoners and ontologies and provide a state-of-the-art insight, into the field of OWL reasoning.

关键词： Ontology

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Adding semantics to stateful Web Services

Adding semantics to stateful Web Services

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5th Workshop on Theory and Practice of Information Technologies - Applications and Theory, ITAT 2005

作者： Babík, Marian Hluchý, Ladislav Department of Parallel and Distributed Computing Institute of Informatics Slovak Academy of Sciences Slovakia

ISBN: (纸本)8070976098

Web Service Resource Framework (WSRF) is a recent effort of the grid community to facilitate modeling of the stateful services. Semantic Web Services (SWS) initiative is proposing a standard called OWL-S, to help the intelligent service provisioning i.e. automated discovery, composition and invocation of the services. In this paper we provide an overview of both frameworks and analyse their complementary features. We will also describe the OWL-S extensions necessary to cover the services introduced by the WSRF standard. Analysis of the requirements as well as description of the architecture is provided. A flood-forecasting application scenario is described, which have been used to evaluate the architecture.

关键词： Websites

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A novel approach for developing interoperable services in cloud environment

A novel approach for developing interoperable services in cl...

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27th International Conference on Information Networking, ICOIN 2013

作者： Nguyen, Binh Minh Tran, Viet Hluchy, Ladislav Department of Parallel and Distributed Computing Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia

ISBN: (纸本)9781467357401

Cloud computing has seen a tremendous growth in the last five years. Along with the growth, many cloud models have been marketed. They deliver hardware and software as virtualization enabled services to users. Although cloud computing offers considerable advantages such as unlimited resources, manageability and lower investment costs but there are still barriers to exploit it. One of the barriers is the difficulties, which users are being faced when developing and deploying their own services into clouds. In this paper, we present a novel approach for developing interoperable services that can be deployed in different cloud infrastructures at the same time. The approach provides an instrument with emphasis on abstraction, inheritance and code reuse. Using the approach, cloud-based services are developed easily by extending existing abstractions classes provided by the instrument or other developers. The interoperability between different clouds is solved via the basic abstraction class of the instrument and all services are inherited and benefited from this advantage. © 2013 IEEE.

关键词： Cloud computing

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PERFORMANCE OF parallel SPANNING TREE ALGORITHMS ON LINEAR ARRAYS OF TRANSPUTERS AND UNIX SYSTEMS

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parallel computing 1991年第4-5期17卷 527-551页

作者： DAS, SK YANG, CQ Center for Research in Parallel and Distributed Computing Department of Computer Sciences University of North Texas P.O. Box 13886 Denton TX 76203-3886 USA

This paper presents empirical performance of parallel algorithms for connected graphs on the transputer and Unix systems, where processes are configured as a one-dimensional array. We conduct experiments for implementing (i) spanning tree algorithm (SPT-list) with unordered list of edges as input, (ii) SPT algorithm with adjacency matrix, (iii) minimum spanning tree algorithm (MST) with weight matrix as input, and (iv) SPT algorithm derived from MST algorithm by assigning each edge-weight equal to 1. The empirical study is performed with a wide range of random graphs, generated for various (uniformly distributed) edge-densities (d) for a given number (n) of vertices. We plot curves for resulting speedups as functions of n, d, and p. The edge-density d is varied between 0.1 and 0.9;maximum number of vertices (or edges) considered are 300 (or 40 000) and 500 (or 110 000) for transputer and Unix systems, respectively;and the number of processors p varies from 1 through 8 in the transputer system while 1 through 4 in the Unix system. A maximum speedup of 2.98 is obtained on transputers, and that for the Unix system is 3.0. We observe that the speedups of all algorithms vary with increasing number of vertices or edge-density. However, employing more processing units in an algorithm does not necessarily enhance its speedup because of additional communication overhead.

关键词： TRANSPUTERS LINEAR ARRAY PERFORMANCE STUDY parallel GRAPH ALGORITHM SPANNING TREE MINIMUM SPANNING TREE

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Optimizing description logics reasoning for the semantic-based Grid repositories

Optimizing description logics reasoning for the semantic-bas...

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3rd International Conference on Semantics, Knowledge, and Grid, SKG 2007

作者： Babik, Marian Hluchy, Ladislav Department of Parallel and Distributed Computing Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia

ISBN: (纸本)0769530079

The Semantic Grid is a recent initiative to expose semantically rich information associated with Grid resources to build more intelligent Grid services. Recently, several projects have embraced this vision and there are several successful applications that combine the strengths of the Grid and of semantic technologies. However, Semantic Grid still lacks a technology, which would provide the needed scalability and integration with existing infrastructure. In this paper we present our on-going work on a semantic grid repository, which is capable of addressing complex schemas and answer queries over ontologies with large number of instances. We present the details of our approach and describe the underlying architecture of the system. We conclude with a performance evaluation, which compares the current state-of-the-art reasoners with our system. © 2007 IEEE.

关键词： Query processing

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