Preference-based AI Planning for Web Service Composition

Vallejos, Sebastián; Araujo, Leonardo Da Rocha; Rodríguez, Guillermo; Berdún, Luis; Toscani, Renzo

doi:10.1109/tla.2020.9398640

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Most service composition methods take into account the user's QoS. These QoSs include both hard requirements [12][13][14][15], such as time limits [3,[16][17][18] and costs [6,[19][20][21][22], and soft requirements [11,18,[23][24][25][26][27][28][29][30][31][32], such as security [4,5,9,10,33], reliability and flexibility.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

2023

J Cloud Comp

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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.

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