Abstract
Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.
Highlights
Web services are becoming one of the most important interoperable technologies for connecting heterogeneous applications across the Internet to realize cross-platform, cross-system, and cross-language interaction [1]
Many researchers believe that when users select a service, they should consider the functional requirements of users, and non-functional indicators provided by the service—namely, the quality of service (QoS) [2,3]
We propose a blockchain-based matrix factorization prediction method that largely eliminates the interference of unreliable users in QoS predictions, improving the accuracy of
Summary
Web services are becoming one of the most important interoperable technologies for connecting heterogeneous applications across the Internet to realize cross-platform, cross-system, and cross-language interaction [1]. According to the function of web services, users can find and invoke a web service (e.g., a travel service) to build a high-quality service-oriented system, without concern for its programming language, operating platform, or how it is implemented, among many other advantages. Many researchers believe that when users select a service, they should consider the functional requirements of users, and non-functional indicators provided by the service—namely, the quality of service (QoS) [2,3]. It is worth noting that QoS is a set of non-functional attributes, such as availability, response time, execution time, and throughput rate. From the perspective of the server, the QoS attributes are user-independent because the QoS on the server-side presents the same attribute values to all users, such as price, attention, and availability
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