International Journal of Web and Grid Services (6 papers in press)

Regular Issues

• Enhancing Priority based Adaptive Resource Allocation for High Performance Computing Platforms  
  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  
  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
   
  
• Smart Waste Management with IoT: a Pilot Project for Cost-Efficient Solutions in the City Centre of Bratislava  
  by Vanda Klu?ariková, Tobias Knayer, Natalia Kryvinska 
  Abstract: The internet of things and its applications have been exponentially coming to the forefront over the past years. Waste management is one of the areas where IoT has a potential of making a significant difference to our everyday lives. While a variety of smart waste management solutions has already been studied and applied, majority of them are focuses on larger-capacity bins with lower frequency of collection. Contrariwise, this work concentrates on a lower-volume bins with daily frequency of collection bins administered by Municipality of Bratislava, mainly located in the city centre. We study the current waste management situation, as well as potential costs and benefits of a particular smart waste management solution. A solution provided by a Bratislava-based provider of smart waste management solutions, is used as an example of possible IoT applications to the discussed bins. We conclude that given our assumptions, implementation of such a solution would be beneficial. Based on the results of our analysis, we also define limitations of the solution that may be overcome through adoption of recommended measures.
  Keywords: IoT; smart waste management; cost-benefit analysis; cost-efficiency; pilot project; real-time data.
  DOI: 10.1504/IJWGS.2026.10075944
   
  
• Multi-Version and Energy-Efficient Role-Based Transaction Processing for AI Services  
  by Tomoya Enokido, Dilawaer Duolikun, Shigenari Nakamura, Makoto Takizawa 
  Abstract: In Artificial Intelligence (AI) services, a vast amount of data is amassed from various services and devises into Data Centers (DCs). Numerous users share the data by issuing transactions. Consequently, the electricity consumption of DCs increases by the proliferation of AI services. Hence, a control method to maintain data integrity and improve the throughput of transaction processing while reducing the electricity consumption of servers has to be realized for AI services. In this paper, an MVEERO (Multi-Version Energy Efficient Role Ordering) scheduler is newly proposed to maintain data integrity and improve the throughput of transaction processing while reducing the electricity consumption of servers. In evaluation, the execution time of transactions and the electricity consumption of a server cluster in the MVEERO scheduler are shown to be maximally reduced 31% and 13%, respectively, to the EERO-VM (Energy-Efficient Role Ordering in Virtual Machine environment) scheduler which is previously proposed in our studies.
  Keywords: Transaction Processing; Energy-Aware System; Role-Based Scheduler; Multi-Version Concurrency Control; MVEERO Scheduler; RBAC Model; AI Service; Data Center; Electricity Consumption; Virtual Machine.
  DOI: 10.1504/IJWGS.2026.10076080
   
  
• HITT: Heterogeneous Imbalance-Text Transformer for Web Service Representation  
  by Guosheng Kang, Jianhua Feng, Yong Xiao, Jianxun Liu, Buqing Cao 
  Abstract: The explosive growth of web services complicates developer selection. While service representation is key for intelligent management, existing methods rely on textual semantics or network structure alone, often neglecting deep multi-feature fusion and text imbalance or absence across nodes. This paper proposes a transformer model empowered by heterogeneous networks to unify context-aware text and heterogeneous structure encoding. Heterogeneous structure information is incorporated into each transformer layer to capture node/edge information, handling nodes with or without text. A fully-connected attention mechanism integrates representations from text-rich neighbours, textless neighbours, and the nodes own content at each layer. To fully fuse features, a specialised transformation matrix projects different node types into a shared latent space. Experiments show our method outperforms the strongest baselines by nearly 1% in LogLoss and 2% in AUC.
  Keywords: Web Service; Representation Learning; Service Recommendation; Service Classification; Heterogeneous Network.
  
  

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.