Forthcoming Articles

International Journal of Web and Grid Services

International Journal of Web and Grid Services (IJWGS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Web and Grid Services (3 papers in press)

Regular Issues

  • Enhancing Priority based Adaptive Resource Allocation for High Performance Computing Platforms   Order a copy of this article
    by Lung-Pin Chen, Fang-Yie Leu, Chia-Chen Kuo, Ming-Jen Wang, Kun-Lin Tsai 
    Abstract: High-performance computing platforms accelerate rendering application execution by efficiently distributing workloads across clusters of computing hosts. Priority-based scheduling offers a simple and effective mechanism for computing resource allocation, often aligned with pay-per-use models. Traditional priority calculation methods often overlook inter-user parameters, such as competing user priorities and system scales. This paper presents an enhanced adaptive resource allocation strategy that introduces two normalisation techniques: priority scaling and weight sharing. By balancing fairness and responsiveness, the proposed method allows short jobs to complete earlier and avoids queue congestion, resulting in a more efficient and user-friendly environment for rendering workloads with diverse job types and priority levels. Experimental results show that this adaptive approach significantly reduces waiting times with marginal impact on the completion time of high-priority tasks.
    Keywords: Cloud computing; priority-based scheduling; render farm; resource allocation.
    DOI: 10.1504/IJWGS.2025.10073754
     
  • Reputation Measurement for Online Services based on CP-Nets Learning and Aggregation   Order a copy of this article
    by Qianzhi Yin, Xiaodong Fu, Fei Dai, Li Liu, Yan Feng, Jiaman Ding 
    Abstract: The measurement of online service reputation based on ordinal preferences has been proposed to address the issue of unreliable reputation measurement results due to inconsistent user evaluation criteria. When users' complete ordinal preferences are unavailable, these methods ignore unknown preferences or use collaborative filtering to predict preferences without verifying the accuracy of preference prediction, leading to an untrustworthy service reputation. This study proposes an approach that models users' complete preferences using the conditional preference networks (CP-nets) and then measures service reputation by aggregating CP-nets. The approach designs an adaptive Tabu search algorithm to learn users' CP-nets efficiently and aggregating all the CP-nets using the Ranked Pairs method. The service reputation ranking is then deduced from the aggregated CP-net. Experimental results on real datasets show that the proposed method is more efficient compared to existing methods, with more accurate preference prediction, and the reputation ranking is more consistent with user preferences.
    Keywords: reputation measurement; online service; incomplete ordinal preference; CP-nets; ranked pairs.
    DOI: 10.1504/IJWGS.2026.10074644
     

Special Issue on: Emerging Technology in Big Data, AI, and Green Learning Driving Towards Sustainable Web Service Development

  • Optimality and Scalability of Semantic Web Service Composition with Hierarchical Parameter Relationship
    by Jung-Woon Yoo 
    Abstract: Semantic web service composition considers semantics for finding better solutions than syntactic web service composition. This paper focuses on hierarchical relationships among parameters of web services. A comprehensive mathematical model for semantic web service composition, into which hierarchical parameter relationships are incorporated, is presented as a general mathematical formulation. Experimental results demonstrate that the mathematical model for semantic composition finds hidden and better solutions that syntactic composition cannot find. The optimality of the solutions is empirically verified through extensive experiments. Furthermore, the scalability of the model is tested by comprehensive experiments to explore the impacts of eight key factors on web service composition. The mathematical model and the provided data sets are expected to serve as benchmarking tools for performance evaluation of heuristic algorithms for semantic web service composition. Finally, a web application is presented to visualize the semantic web service composition process, which is developed using the Django framework.
    Keywords: AI Planning; Web Service Composition; Semantics; Parameter Hierarchy; Mathematical Modeling.

 

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