Resource allocation and scheduling in the intelligent edge computing context

With the rapid development of the edge computing, Internet economy has become a new situation in economic development. What has brought development to the Internet economy is a variety of family-based platforms (i.e., edge computing). However, in the face of huge Internet users and different users' shopping habits, reasonably personalized e-commerce platform allocations for users can improve the user's website shopping experience. However, due to the huge user data and the diversity of SDN (software defined network) platforms, it is a huge challenge to reasonably allocate e-commerce resources/platforms to users. The quality of the SDN personalized resource allocations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can allocate the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized allocation faces the problem of too high user data dimensions. Therefore, we first conduct computational efficiency analysis toward each user. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that, in the edge computing context, our method can reduce the complexity of resource allocation toward complex data and improve the quality of the allocated data. (C) 2021 Elsevier B.V. All rights reserved.