Towards Green Service Composition Approach in the Cloud

Wang, Shangguang; Zhou, Aoying; Bao, Ruo; Wu, Chang

doi:10.1109/services51467.2021.00025

Cited by 2 publications

(4 citation statements)

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“…Suppose the file size of each subservice is a random number between [1,100]. According to the relevant literature, the sending rate is much higher than the receiving rate, so it is assumed that the receiving efficiency (energy consumption per unit size file) is a random number between [1,50], and the sending efficiency (energy consumption per unit size file) is a random number in [50,150]. We mainly examine the total energy consumption of transfer files and their size (Total file size).…”

Section: Energy Consumption Of Different Methodsmentioning

confidence: 99%

“…With more attention being given to the energy consumption of computing systems, energy-aware service composition has also been emphasized by some researchers [49,50]. D. Zeng et al [51] solved the energy-efficient service composition problem in a green energy-powered cyber-physical fog system, and jointly considered source rate control, load balancing, and service replica deployment.…”

Section: Related Workmentioning

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: Energy Consumption Of Different Methodsmentioning

confidence: 99%

Section: Related Workmentioning

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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“…Fortunately, SDN in centralized network management brings some inspirations to latency‐sensitive networks. Although there are many different routes between the mobile devices and the servers in SDN design, the performance of the whole network depends on the ability of the controller to determine routes 10 . With the increase of computing requests, these computing requests need to be reasonably allocated to different servers to reduce latency, thereby ensuring overall network performance 11 …”

Section: Introductionmentioning

confidence: 99%

“…Although there are many different routes between the mobile devices and the servers in SDN design, the performance of the whole network depends on the ability of the controller to determine routes. 10 With the increase of computing requests, these computing requests need to be reasonably allocated to different servers to reduce latency, thereby ensuring overall network performance. 11 In our paper, SDN is integrated with MEC to determine the workflow scheduling, and complex service chain scenes are able to be realized through traffic-oriented rules.…”

Section: Introductionmentioning

confidence: 99%

Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment

Cao

Geng

et al. 2020

Concurrency and Computation

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Edge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software-defined network (SDN)-based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm-III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time.

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Towards Green Service Composition Approach in the Cloud

Cited by 2 publications

References 0 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

Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment

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