Service composition considering energy consumption of users and transferring files in a multicloud environment

Abstract

In the internet and cloud environment, service composition is always used to enhance the function and processing ability of clouds. Those clouds work together for a user and provide different functions. A service request may involve multiple clouds. The past work focuses on the method of service composition and ignores the energy composition when files are transferred between clouds, including the energy consumption for transferring files (sending files from the user to the cloud and receiving files from the cloud to the user) of the user. The paper models the service composition in a multicloud environment. Based on those models, we use the GA (genetic algorithm) algorithm (GA-C) to solve the service composition problem with multiple targets in a multicloud environment. Simulation results show that the GA-C can: (1) reduce the average number of involved clouds and the energy consumption between clouds, and (2) reduce the energy consumption of the user and the failure rate of service composition.

Skyline-Enhanced Deep Reinforcement Learning Approach for Energy-Efficient and QoS-Guaranteed Multi-Cloud Service Composition

“…Furthermore, the fuzzy continuous neighborhood search method is adopted to enhance the local search capability of the algorithm. Li et al [30] focused on the energy consumption of file transfer between clouds. Based on the multi-cloud environment, they first standardized the QoS of services.…”

“…In a multi-cloud environment, the energy consumption model is a crucial concept because the energy consumption of cloud services directly affects the overall operating costs and environmental issues, such as energy conservation and cloud platform emission reduction. We believe that the energy consumption of cloud services in a multi-cloud service composition is composed of two parts, the energy consumption generated by the execution of the cloud service itself and the energy consumption generated by file transmission between cloud services [30,49]. For any cloud service, its execution energy consumption can be calculated based on two parameters, execution time and energy consumption efficiency.…”