Addressing Compatibility Challenges in Multi-Cloud Services: A Markov Chain-Based Service Recommendation Framework

Service recommendation aims to assist users in selecting appropriate services according to their requirements while ensuring seamless compatibility in modern cloud and edge computing environments. In dynamic multi-cloud scenarios, services are typically deployed across heterogeneous cloud platforms and are frequently reconfigured. However, most existing service recommendation approaches primarily focus on static compatibility aspects, such as service interfaces or communication protocols, while overlooking the dynamic characteristics of service interactions. However, several limitations can be identified. First, the lack of effective mechanisms for quantifying service compatibility in dynamic cloud environments often leads to degraded system efficiency. Second, the absence of dedicated multi-cloud service compatibility quantification methodologies restricts recommendation accuracy. Third, insufficient mathematical analysis with respect to uniqueness, feasibility, and correctness may result in unstable evaluation outcomes and additional computational overhead. To overcome these limitations, this paper presents McCom, a multi-cloud service recommendation framework designed to quantify service compatibility performance and address the aforementioned challenges. First, a novel Markov chain-based compatibility quantification model is developed to characterize service interactions in dynamic multi-cloud environments. By exploiting the homogeneity, irreducibility, and convergence properties of Markov chains, the proposed model enables stable and reliable compatibility assessment. Second, a multi-cloud compatibility quantification strategy is introduced to mitigate interference arising from complex service pools through refined filtering and sketching mechanisms. Third, a series of mathematical proofs are provided to rigorously demonstrate the feasibility, correctness, and uniqueness of the proposed quantification method. Extensive simulation results indicate that the proposed framework achieves significant performance improvements, including enhancements in recommendation quality (14.44% in F1 score), reductions in latency (40.68%), and increases in accuracy (50.85%), compared with existing state-of-the-art approaches.