DCEM: A data cell evolution model for service composition based on bigraph theory

Lu, Jiawei; Zhou, Huan; Zhu, Haotian; Zhang, Yuanming; Liang, Qianhui; Xiao, Gang

doi:10.1016/j.future.2020.05.006

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Therefore, the problem of service composition optimization selection in cloud manufacturing is more complex than that applied in traditional cloud computing environments [19]. The existing approaches on service composition mainly focus on four aspects: business flow-based [20], graphbased [21], agent-based [22], and QoS-based service composition methods [23]. QoSbased service composition methods are the research hotspots of all the above service composition methods, and the research on QoS-based service composition methods is divided into the following three main categories.…”

Section: Literature Reviewmentioning

confidence: 99%

A matching-synergy degree-based optimization selection method for cloud manufacturing service composition

Chao¹,

Li²,

2023

Preprint

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In a cloud manufacturing scenario, the matching degree between manufacturing tasks and cloud services, as well as the synergy degree between multiple selected cloud services are key metrics to measure the benefits of cloud manufacturing service application. However, the latter is often overlooked in the cloud service selection process. In this paper, a matching-synergy analysis-based optimization method of cloud manufacturing service composition is proposed. Firstly, an evaluation system of cloud service composition quality is established, including service matching degree (SM), service composition synergy (CS), and other metrics, such as service time (T), service cost (C) and reliability (R). Secondly, considering the interests of both service requestors and service resource providers, a two-constraint combination preference model is constructed, and solved by using the improved ant colony algorithm (IACO). Finally, the feasibility and effectiveness of the proposed method are verified with the example of an automobile bumper cloud service.

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Section: Literature Reviewmentioning

confidence: 99%

A matching-synergy degree-based optimization selection method for cloud manufacturing service composition

Chao¹,

Li²,

2023

Preprint

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“…M. Sun et al [30] proposed an energy-efficient mechanism to optimize Internet of Thing (IoT) service compositions that IoT devices save energy consumption by mutual collaboration. J. Lu et al [31] proposed a data cell evolution model (DCEM) that combines data service information and biological cell behavior analysis to encapsulate data services into data cells, and then they used bigraph theory to guarantee the consistency of service evolution.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

Yin

Hao

2020

IEEE Access

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Service composition is always employed to enhance function by composing atomic services that reside in different clouds together. In a multi-cloud environment (MCE), the time and energy consumption of service composition may differ because of the dynamic of the network. The network varies between clouds, or even between composite requests and clouds. A cloud provides a limited variety of services, which makes a composite request that requires multiple clouds to jointly compose all atomic services together. All of this makes it more difficult than ever to implement energy-aware service composite in a MCE. In this paper, we model service composition in the MCE and propose an energy-aware multiple targets service composition method for executing atomic services in a composite request. Since the proposed method gets the scheduling by searching all possible mappings between service request blocks (a set of requests to multiple atomic services) and clouds, we call it "All-Search". To reduce the complexity of All-Search, we propose "Itersplit", a heuristics algorithm that can achieve multiple targets. The performance of All-Search, Cloud-SEnergy, and Itersplit is tested through simulations. The results in multiple aspects indicate that Itersplit performs better than other algorithms when we take Itersplit-20%.

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A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition

Jiang

Cheng

et al. 2022

Computing

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DCEM: A data cell evolution model for service composition based on bigraph theory

Cited by 4 publications

References 39 publications

A matching-synergy degree-based optimization selection method for cloud manufacturing service composition

A matching-synergy degree-based optimization selection method for cloud manufacturing service composition

An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition

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