Scholarlink

<p>Availability and reliability are essential prerequisites for web service-based systems to function seamlessly. However, dynamic operating environments expose web services to potential challenges like failures, unavailability, and fluctuating quality of service. Such failures in any component service can degrade the performance of an entire composite service. This paper presents a proactive web service selection approach that handles runtime failures by identifying optimal replacement candidates during the initial composition stage. The proposed method combines multi-criteria QoS evaluation using the PROMETHEE Plus method, checks for semantic similarity through a determinant-based IOPE (Input, Output, Precondition, Effect) matchmaking mechanism, and uses a genetic algorithm to optimize service composition plans. First, QoS attributes of candidate services and user requirements are normalized to a common scale. They are then evaluated using PROMETHEE Plus, which produces a prioritized ranking based on Net Outranking Flow scores. At the same time, functional compatibility between the requested and candidate services is assessed through IOPE-based semantic matchmaking. This involves constructing weighted bipartite graphs and solving them using a determinant-based maximum-matching approach to compute similarity scores and ranks. Next, these QoS rankings and semantic similarity measures are combined to estimate service replaceability. Finally, a genetic algorithm evolves candidate compositions iteratively. Each chromosome represents a service combination. A hybrid fitness function that combines QoS attributes and replaceability scores guides selection toward optimal, resilient composition plans. Experimental results demonstrate that the method achieves superior selection quality compared to existing techniques, with average improvements of 9.7% in F1-score and 7.89% in G-mean. These findings highlight the effectiveness of integrating functional behavior with QoS-driven optimization. Overall, the approach enhances service resiliency by enabling immediate, reliable replacements for failing services, offering a unique and comprehensive solution for robust composite service execution in dynamic environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

WSSR: an approach for web service selection based on replaceability

  • 期刊论文

  • 发表日期：

    2026年5月20日

查看全文
  • Lalit Purohit,
  • Santosh Singh Rathore,
  • Sandeep Kumar

摘要

Availability and reliability are essential prerequisites for web service-based systems to function seamlessly. However, dynamic operating environments expose web services to potential challenges like failures, unavailability, and fluctuating quality of service. Such failures in any component service can degrade the performance of an entire composite service. This paper presents a proactive web service selection approach that handles runtime failures by identifying optimal replacement candidates during the initial composition stage. The proposed method combines multi-criteria QoS evaluation using the PROMETHEE Plus method, checks for semantic similarity through a determinant-based IOPE (Input, Output, Precondition, Effect) matchmaking mechanism, and uses a genetic algorithm to optimize service composition plans. First, QoS attributes of candidate services and user requirements are normalized to a common scale. They are then evaluated using PROMETHEE Plus, which produces a prioritized ranking based on Net Outranking Flow scores. At the same time, functional compatibility between the requested and candidate services is assessed through IOPE-based semantic matchmaking. This involves constructing weighted bipartite graphs and solving them using a determinant-based maximum-matching approach to compute similarity scores and ranks. Next, these QoS rankings and semantic similarity measures are combined to estimate service replaceability. Finally, a genetic algorithm evolves candidate compositions iteratively. Each chromosome represents a service combination. A hybrid fitness function that combines QoS attributes and replaceability scores guides selection toward optimal, resilient composition plans. Experimental results demonstrate that the method achieves superior selection quality compared to existing techniques, with average improvements of 9.7% in F1-score and 7.89% in G-mean. These findings highlight the effectiveness of integrating functional behavior with QoS-driven optimization. Overall, the approach enhances service resiliency by enabling immediate, reliable replacements for failing services, offering a unique and comprehensive solution for robust composite service execution in dynamic environments.