A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics

Li, Xiu; Song, Jingdong; Huang, Biqing

doi:10.1007/s00170-015-7804-9

Cited by 74 publications

(37 citation statements)

References 40 publications

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“…Lin and Chong (2017) addressed resource constraint project scheduling for solving computing resource allocation problems in a cloud manufacturing system by proposing a genetic algorithm (GA) incorporating a number of new ideas (enhancements and local search). Laili, Zhang, and Tao (2011) dealt with collaborative design task scheduling in cloud manufacturing by designing a new energy adaptive immune GA. Li, Song, and Huang (2016) presented an architecture of scientific workflow management system based on a cloud manufacturing service platform and proposed a novel workflow scheduling algorithm called max percentages.…”

Section: Service and Task Schedulingmentioning

confidence: 99%

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

Liu

Wang

et al. 2018

International Journal of Production Research

221

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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 and Task Schedulingmentioning

confidence: 99%

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

Liu

Wang

et al. 2018

International Journal of Production Research

221

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“…Uncritical analysis of poorly understood data-sets does not generate knowledge. As Li et al (2015:3) acknowledge, despite the increasing availability of data, how BD can be used to support decision-making is "an enormous challenge". Whatever the size of the data-set, it needs appropriate analysis to create useful information that reveals what is significant within the data.…”

Section: The Big Idea Of Big Datamentioning

confidence: 99%

Making sense of Big Data – can it transform operations management?

Matthias

Fouweather

Gregory

et al. 2017

IJOPM

114

103

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Structured Abstract:Purpose This paper focuses on the application and exploitation of Big Data to create competitive advantage. It presents a framework of application areas and how they help the understanding of targeting and scoping specific areas for sustainable improvement. Empirical evidence demonstrates the application of Big Data in practice and tests the framework. Design/methodology/approachAn exploratory approach is adopted to the secondary research which examines vendors' offerings. The empirical research used the case study method. FindingsThe findings indicate that there is opportunity to create sustainable competitive advantage through the application of big data. However there are social, technological and human consequences that are only now beginning to emerge which need to be addressed if true long-term advantage is to be achieved. Research LimitationsThe research develops a framework and tests it only in 2 dimensions. This should be expanded. The vendor analysis limitations lie within the nature of the information available and the difficulties in mitigating against bias. Practical ImplicationsThe suggested framework can help academics and managers to identify areas of opportunity to do so, setting new levels of performance and new agendas for business. Originality/valueThis work contributes to service operations management, building on Kranzberg (1986) and the impact of technology and on Fosso by developing a systems application framework to further understanding of big data from a practical perspective to extend their research taxonomy insights. Our case studies demonstrate how the use of BD enhances operational performance.

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“…Deploying time is the time required to deploy analytic service on the cloud resource, which is represented in Equation 13. Total execution time Total_Exec_Time AR ASP i CR k represented in Equation 14 for AR j by ASP i on cloud resource CR k is the sum of processing time written as Process_Time AR ASP i CR k and the deploying time of ASP i on cloud resources CR k written as Deploy_Time ASP i CR k…”

Section: Fitness For Resource Schedulingmentioning

confidence: 99%

Optimal provisioning and scheduling of analytics as a service in cloud computing

Moorthy

Pabitha

2019

Trans Emerging Tel Tech

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We propose an analytic request‐centric multiobjective provisioning and scheduling method based on hybrid particle swarm optimization, called analytics‐as‐a‐service (AaaS) provisioning and resource scheduling (APARS) system, to optimally provision analytics service and schedule cloud resources by taking into account of user specified constraints. Analytics‐as‐a‐service provisioning and resource scheduling is employed to maximize accuracy of the analytic service and minimize the execution time and cost of the analytic request by combining multiobjectives with the constraint of minimal deadline and budget. Provisioning AaaS and scheduling cloud resources for analytic request is challenging as the analytic request requires scaling up and down of cloud resources dynamically. Thus, this domain invites a rigid system that can provide optimal analytics using cloud services for specified quality of service parameters. Our proposed system can handle diverse set of analytic requests with the goal of minimizing execution time, cost, critical performance, load balancing level and maximizing resource utilization, AaaS utilization, and success rate. The proposed APARS system is tested and evaluated under different use cases and compared with other metaheuristic algorithms, and we found that the results to be convincing our claims. The experimentation is carried out for various scenarios and the proposed APARS is evaluated with other metaheuristic algorithm. Simulation results validate the APARS system and show that the proposed method performs better than other existing methods with various use cases.

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A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics

Cited by 74 publications

References 40 publications

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

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

Making sense of Big Data – can it transform operations management?

Optimal provisioning and scheduling of analytics as a service in cloud computing

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