Fast Multi-Criteria Service Selection for Multi-User Composite Applications

Kurdija, Adrian Satja; Silic, Mario; Delač, Goran; Vladimir, Klemo

doi:10.1109/tsc.2019.2925614

Cited by 8 publications

(4 citation statements)

References 50 publications

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“…A. Souri et al [43] presented a hybrid formal verification approach to assess the service composition in multicloud environments targeted at reducing the number of clouds of a service request and meeting the required level of quality of service (QoS). Other service composition methods we introduce in "Simulation and comparisons" section include All-C [44][45][46], Bas-C [44][45][46], Smt-C [44][45][46], Swm-C [47], and Fast-C [48].…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we will compare our method GA-C with All-C [44][45][46], Bas-C [44][45][46], Smt-C [44][45][46], Swm-C [47], and Fast-C [48].…”

Section: Simulation Environmentmentioning

confidence: 99%

“…Fast-C [48]: Fast-C is a heuristic method that considers multiple criteria services selection, and tries to satisfy as many QoS requirements as possible. It takes a globalaware utility cost based on expected compositional QoSs and improves the solution by iterations.…”

Section: Simulation Environmentmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…In this section, we will compare our method GA-C with All-C [44][45][46], Bas-C [44][45][46], Smt-C [44][45][46], Swm-C [47], and Fast-C [48].…”

Section: Simulation Environmentmentioning

confidence: 99%

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

Zhu

2023

J Cloud Comp

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“…al. [14] proposed service selection algorithm using heuristic method for multiple criteria., The proposed method reduces the problem to transportation problems. The cost based utility function is is used to improve solution.…”

Section: Non-correlation Aware Selectionmentioning

confidence: 99%

Qos Correlation with Constraint Based Optimal Service Selection in Composition

Dongre¹,

Ingle²

2021

JUSST

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In a service-oriented architecture, service composition plays an important role as a key technology for integrating complex web applications. The service selection as part of the service composition process attracted towards Quality of Service as mandatory criteria for optimal selection of services for the composition process. While fulfilling the non-functional requirements from the user among a functionally similar list of services is a very challenging task. Web services are treating Software as a Service for building web applications. The Quality of Service correlation of services with constraints for selection of service is proposed in this paper. Experimental results of correlation without constraint method and proposed method show that our method outperforms over existing approach.

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Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

2023

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Modern service-based processes in mobile environments are highly complex due to the necessary spatial–temporal coordination between multiple participating users and the consideration of context information. Due to the dynamic nature of mobile environments, disruptive events occur at runtime, which require a re-selection of the planned service compositions respecting multiple users and context-awareness. Thereby, when re-selecting services the features performance, solution quality, solution robustness and alternative solutions are essential and contribute to the efficacy of service systems. This paper presents an optimization-based heuristic technique based on a stateful representation that uses a region-based approach to re-select services considering multiple users, context information and in particular disruptive events at runtime. The evaluation results, which are based on a real-world scenario from the tourism domain, show that the proposed heuristic is superior compared to competing artifacts.

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Fast Multi-Criteria Service Selection for Multi-User Composite Applications

Cited by 8 publications

References 50 publications

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

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

Qos Correlation with Constraint Based Optimal Service Selection in Composition

Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

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