New Advances of the Research on Cloud Simulation

Li, Bo Hu; Chai, Xudong; Zhang, Linlin; Hou, Baocun; Lin, Ting; Yang, Chen; Xiao, Yingying; Xing, Chi; Zhang, Zhihui; Zhang, Yabin; Tan, Li

doi:10.1007/978-4-431-54216-2_17

Cited by 16 publications

(11 citation statements)

References 1 publication

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“…According to the reliability calculation method, we will calculate the reliability value of the FRCPN. tSet.get (1).getPRWSS().get(0).getTrustValue() tSet.get (1).getTrustValue(),…,_ tSet.get(n).getPRWSS().get(0).getTrustValue() tSet.get(n).getTrustValue(),).…”

Section: An Example Of the Application Of The Methodsmentioning

confidence: 99%

“…The five kinds of production rule, above have some common points; the points can be summed up with some general rules: (1) In the first, fourth, and fifth production rules, the reliability value of the postpositional Web service of the transition equals the product of the reliability of the prepositional Web service of the transition and the reliability value of the transition. (2) In the second production rule, the reliability value of the postpositional Web service of the transition equals the minimum reliability value of all the prepositional Web services multiplied by the reliability value of the transition.…”

Section: Of 21mentioning

confidence: 99%

“…According to Figure 8, we can know the relationships among the reliability values of Web service compositions, the number of Web services, and the reliability ranges of transitions. The following relationships can be obtained: (1) Under the same reliability range of transitions, if the number of Web services is greater, then the length of SLLFRV is longer and the reliability values of the Web service compositions are smaller. (2) Under different reliability ranges of transitions, if the minimum range is smaller, then as the number of Web services increases, the reliability values of the service compositions decrease more rapidly.…”

Section: Simulated Experimentsmentioning

confidence: 99%

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A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform

Deng¹,

Chen

et al. 2016

Future Internet

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In order to develop a Supercomputing Cloud Platform (SCP) prototype system using Service-Oriented Architecture (SOA) and Petri nets, we researched some technologies for Web service composition. Specifically, in this paper, we propose a reliability calculation method for Web service compositions, which uses Fuzzy Reasoning Colored Petri Net (FRCPN) to verify the Web service compositions. We put forward a definition of semantic threshold similarity for Web services and a formal definition of FRCPN. We analyzed five kinds of production rules in FRCPN, and applied our method to the SCP prototype. We obtained the reliability value of the end Web service as an indicator of the overall reliability of the FRCPN. The method can test the activity of FRCPN. Experimental results show that the reliability of the Web service composition has a correlation with the number of Web services and the range of reliability transition values.

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Section: An Example Of the Application Of The Methodsmentioning

confidence: 99%

Section: Of 21mentioning

confidence: 99%

Section: Simulated Experimentsmentioning

confidence: 99%

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A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform

Deng¹,

Chen

et al. 2016

Future Internet

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“…COSIM-CSP is a collaborative simulation framework that provides a platform on which developers can perform multidisciplinary collaborative simulation of virtual prototyping and large scale co-simulation [10]. This paper presented an example of aircraft landing gear virtual prototype collaborative simulation design system based on COSIM-CSP.…”

Section: B Integration With Cosim-cspmentioning

confidence: 99%

A SLA-based scheduling approach for multi-tenant cloud simulation

Peng

Zhao

et al. 2015

2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Modern product development becomes increasingly collaborative and parallel, which raises the need for sharing simulation resources in a multi-tenant simulation environment. However, the service requests and scheduling policies of tenants differ since system resources are shared by tenants. In order to deal with this issue, the framework of monitoring, detecting and scheduling system performance isproposed. As SLA is produced as the contract for negotiations between tenants and the service provider, the WS-Agreement specification is applied and improved to represent the service contents and qualities. Based on the SLA template, a dynamic resource distributing mechanism is realized. The SatisfactionIndex is introduced into the scheduling of distributed resources, in which the weighted formula is determined by priorities. A case study has been undertaken and preliminary results show that the proposed solution can effectively support the cloud simulation and efficiently meet tenants' personal requirements.

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“…Cloud simulation system consists of soft resource (including simulation model, simulation data, simulation software, simulation knowledge, etc), and hard resource (including computing cluster, semiphysical emulation equipment, etc); simulation capability (the abstract of human, technique and management to support the different stages of activities in Virtual, Constructive, Live simulation, such as the capability of modeling, simulation execution, results analysis, evaluation and application) [4]. For example, Chinese first cloud simulation system has integrated hundreds of simulation software and models, super computers that runs 100 teraflop operations per second and semi-physical emulation equipments that worth billions of dollars.…”

Section: Introductionmentioning

confidence: 99%

A Multi-centric Model of Resource and Capability Management in Cloud Simulation

Lin

Yang

2013

2013 8th EUROSIM Congress on Modelling and Simulation

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The popularity of the modeling and simulation application increases the demand for acquiring the service of the simulation resource and capability (R/C) anytime, anywhere, on demand through the network. Cloud simulation draws the idea of cloud computing that integrates and shares the R/Cs to provide services which significantly depending on the R/Cs' management. Most existing work assumes that there is only one management center, and some existing work uses master-slave management architecture. Both of them cannot provide services reliably in the wide area network. The relevant work in cloud computing deploys datacenters scatted and executes the distributed scheduling reliably. However, it does not take the limited R/C constraint and the parallel interoperability of the sub-tasks into account which are unique in simulation field. Our goal is to re-design an architecture of R/Cs' management with multi-centers in wide area. We also propose a mathematical model for the cross-centric global optimized allocation of R/C services. The model includes the factors of the availability of R/C service and the remote collaborative cost among sub-tasks. We show the model achieves desirable effect in the utilization balance of R/Cs and the reduction of the collaborative cost by experiment compared with an existing decentralized model.

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New Advances of the Research on Cloud Simulation

Cited by 16 publications

References 1 publication

A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform

A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform

A SLA-based scheduling approach for multi-tenant cloud simulation

A Multi-centric Model of Resource and Capability Management in Cloud Simulation

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