Towards Optimal Resources Allocation in Cloud Manufacturing: New Task Decomposition Strategy and Service Composition Model

Abstract

Service composition optimization is one of the core issues in cloud manufacturing research. However, all current studies of service composition in cloud manufacturing assume that tasks have been decomposed into subtasks, so they can be directly mapped to existing services. However, due to the complexity, diversity, and multilevel of services in cloud manufacturing, services have different granularity. Therefore, the matching between tasks and services does not always occur at the lowest level. For solving the problem of discontinuity between task decomposition and service composition, this paper considers the characteristics of existing services in the cloud pool and proposes a task decomposition strategy based on task/service matching on the basis of refining the description model of tasks and services. Then, for the decomposed subtask set, the E-CARGO model is used to model the optimal composition process of services, and CPLEX is used to solve the model. Practical cases show that the proposed task decomposition strategy can solve the problem of discontinuity between task decomposition and service composition without relying on more expert systems. In addition, the proposed service composition model is more flexible, can easily model more variable factors, and CPLEX can solve the model more quickly and stably.