Correlation-aware QoS modeling and manufacturing cloud service composition

Jiang, Hong; Yao, Xifan; Chen, Yong

doi:10.1007/s10845-015-1080-2

Cited by 142 publications

(39 citation statements)

References 43 publications

(43 reference statements)

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“…QoS criteria, objectives and constraints are core elements for building mathematical models of service composition. Regarding objective functions, most papers simply adopted the additive weighting method (Jin, Yao, and Chen 2015), i.e. assigning simply different weights to different QoS criteria.…”

Section: Service Compositionmentioning

confidence: 99%

“…Other particular aspects of tasks, services and their matching method have also been taken into account, including energy consumption (Xiang et al 2014;, correlations (Li, Jiang, and Ge 2014;Jin, Yao, and Chen 2015;Zhou and Yao 2017d), execution reliability (Jing et al 2014), etc. In addition, Liu and Zhang proposed a synergistic elementary service group-based service composition method, which allows free combination of multi-function, equivalent elementary services into a synergistic elementary service group to perform each subtask collectively.…”

Section: Service Compositionmentioning

confidence: 99%

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Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

210

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For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

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Section: Service Compositionmentioning

confidence: 99%

Section: Service Compositionmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

210

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“…The introduction of time enhancement function establishes a trusted service composition model, thus transforming service composition problem into a nonlinear integer coding problem. In [12], the correlation-aware service model is given, and the genetic algorithm is used to find the service composition in cloud manufacturing. In [13], a new gene coding as well as the differential evolution algorithm is used to find the service composition, which improves the convergence of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…While exist unclustered points (4) If (without clustered center) (5) Compute distance and generate center (6) Compute Euclidean distance (7) Generate 1 (8) End if (9) If (exist clustered center) (10) = + 1 (11) Compute distance (12) Take remote point as new center (13) Compute Euclidean distance (14) Generate (15) End if (16) End while (17) End for Algorithm 1: Particle initialization based on parallel covering algorithm.…”

Section: Input: Wsmentioning

confidence: 99%

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

Guo¹,

Chen²,

Zhang³

et al. 2017

Security and Communication Networks

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Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD), the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO). Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results.

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“…However, cloud-based models also pose challenges such as interoperability, reliability, availability, capability, ability, and adaptability of resources and services across spatial boundaries (Luo et al 2013;Khalfallah et al 2014;Wang et al 2015;Chen and Lin 2015;Jin et al 2015;Lin and Chong 2015). The uncertainty is thus an integral part of the cloud-based models.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy holons for intelligent multi-scale design in cloud-based design for configurations

et al. 2015

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Cloud-based design for configurations can be referred to as a service-oriented networked design for configurations model. However, cloud-based models also pose challenges such as reliability, availability, capability, ability, adaptability of resources, and services across spatial boundaries. Multi-scale design can presumably stimulate greater intelligence in cloud-based models. Using the concepts of the fuzzy holon and the fuzzy attractor, this paper proposes the fuzzy holonic approach to address multi-scale design for configurations. A fuzzy design holon is defined through two basic holons: fuzzy function holon and fuzzy solution holon. A fuzzy attractor is defined as a fuzzy function holon or fuzzy function solution toward which a design tends to evolve. The proposed fuzzy holon model is driven by two conflicting drives: (a) completeness of fuzzy function holons and fuzzy solution holons, and (b) discrimination of fuzzy function holons and fuzzy solution holons. Through simulations, four possible states of behavior of fuzzy holon design are found: (a) the impossibility state characterized by the impossibility of fuzzy holon creation; (b) the creation and destruction state sometimes characterized by the creation of fuzzy holons and sometimes the destruction of fuzzy holons, (c) the development state characterized by a natural creation and development of fuzzy holons and (d) the failure state characterized by the interruption of the development of the fuzzy design holon and the destruction of already created fuzzy design holon. The model explains that design is not an orderly and well behaved phenomenon. It shows that fuzzy holon design is a discontinuous phenomenon.Keywords Intelligent design · Cloud-based design and manufacturing · Cloud-based design for configuration · Multi scale design · Fuzzy holon design

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Correlation-aware QoS modeling and manufacturing cloud service composition

Cited by 142 publications

References 43 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark

Fuzzy holons for intelligent multi-scale design in cloud-based design for configurations

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