Cloud service selection based on QoS-aware logistics

Ran, Wenxue; Liu, Huijuan

doi:10.1007/s00500-019-04196-9

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The novelist recommended [28] a way to choose the superlative cloud provider by factoring in QoS considerations. The value of the QoS characteristics is clustered using the Kmeans clustering technique, and similarity is determined by the distance between the clusters.…”

Section: Literature Surveymentioning

confidence: 99%

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Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Khan,

Subramaniam

2023

IEEE Access

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Due to its heterogeneity, cloud services providers' (CSPs) rapid expansion presents several challenges, such as optimal service selection and privacy preservation. Multiple users using the cloud service at once increases the delay for service selection and request. Service interruptions result from centralized provisioning and insecurity. Existing work constraints include access control loss, service disruptions, security issues, trust management issues, and delays. Blockchain-based request scheduling and optimal CSP selection in edge-assisted clouds were presented in this research. Five phases-Data User (DS) authentication, sensitivityaware request scheduling, policy verification, trust management, and optimal CSP selection-are proposed. In the first phase, DU authentication detects and eliminates authorized users. We suggested a chaotic map-based camellia encryption algorithm (CMCE) to boost security. The gateway schedules service requests using Johnson's rule-based Stochastic Gradient Descent method, considering delay, throughput, and priority, in the second phase. This schedules the request into sensitive and non-sensitive services. Policy verification is done in the third phase utilizing Dynamic Policy-based Access control, which allows only sensitive requests. In phase four, we calculate the CSP trust value to boost security. Based on CSP behavior, we introduced the Multi Behavior Analysis-based Nomadic People Optimizer method. Every CSP's trust value is modified based on user feedback over time. Finally, the best CSP is chosen for data user service, and suggested Dynamic and non-cooperative Game Theory is to choose the best CSP from a list. CloudSim is used to simulate and assess.INDEX TERMS Blockchain, Cloud Computing, Edge, Scheduling

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Section: Literature Surveymentioning

confidence: 99%

“…High latency [27] To improve service ranking and selection by taking into account a diversity of quality issues Fuzzy logic poor security [28] To present a technique for picking the best cloud services based on QoS criteria.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Khan,

Subramaniam

2023

IEEE Access

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“…A large number of logistics service resources upload different regions and different types of service contents to the cloud logistics platform through virtualization technology. Logistics demanders from different regions can also release various logistics requests through the cloud logistics platform (Ran and Liu, 2019; Huang et al ., 2020). For example, China's Road Song network freight platform (www.logory.com) is a logistics information platform that integrates logistics service transactions, logistics process management and collaborative process docking.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

Wang

Hu²,

Wang³

2023

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PurposeBy introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.Design/methodology/approachThis paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.Findings(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.Originality/valueThe originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

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Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective

Xu,

Elomri,

Baldacci

et al. 2024

Ann Oper Res

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Industrial 4.0 (I4.0) is believed to revolutionize supply chain (SC) management and the articles in this domain have experienced remarkable increments in recent years. However, the existing insights are scattered over different sub-topics and most of the existing review papers have ignored the underground decision-making process using OR methods. This paper aims to depict the current state of the art of the articles on SC optimization in I4.0 and identify the frontiers and limitations as well as the promising research avenue in this arena. In this study, the systematic literature review methodology combined with the content analysis is adopted to survey the literature between 2013 and 2022. It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. Scholars can take this investigation as a means to ignite collaborative research that tackles the emerging problems in business, whereas practitioners can glean a better understanding of how to employ their OR experts to support digital SC decision-making.

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Cloud service selection based on QoS-aware logistics

Cited by 6 publications

References 10 publications

Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective

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