In order to study the constructing technology of virtual production line, the structure model of virtual production line was presented, and the object oriented technique was used to establish its basic classes and re...In order to study the constructing technology of virtual production line, the structure model of virtual production line was presented, and the object oriented technique was used to establish its basic classes and relevant models, including solid model, behavior model and object oriented Petri net based control model, and based on this, the constructing of virtual production line was realized. The application proved that the virtual production line had many good characteristics, such as visualization, interaction, multi layer and reusability, and it's an efficient tool of analyzing and modeling for layout planning and rapidly reconfiguring of production line.展开更多
On November 26, 2009, ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, was showcasing its Long Term Evolution (LTE) products and solutions to network
With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most c...With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self-Tuning Particle Swarm Optimization approach(called STPSO) is proposed to configure the cloud environment. STPSO can automatically adjust the arbitrary configuration to a valid configuration. To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.展开更多
文摘In order to study the constructing technology of virtual production line, the structure model of virtual production line was presented, and the object oriented technique was used to establish its basic classes and relevant models, including solid model, behavior model and object oriented Petri net based control model, and based on this, the constructing of virtual production line was realized. The application proved that the virtual production line had many good characteristics, such as visualization, interaction, multi layer and reusability, and it's an efficient tool of analyzing and modeling for layout planning and rapidly reconfiguring of production line.
文摘On November 26, 2009, ZTE Corporation (ZTE), a leading global provider of telecommunications equipment and network solutions, was showcasing its Long Term Evolution (LTE) products and solutions to network
基金supported by the Foundation for Outstanding Young Scientist in Shandong Province (No. BS2014DX021)the Fundamental Research Funds for the Central Universities (No. 14CX02136A)the National Natural Science Foundation of China (Grant No. 61402533)
文摘With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self-Tuning Particle Swarm Optimization approach(called STPSO) is proposed to configure the cloud environment. STPSO can automatically adjust the arbitrary configuration to a valid configuration. To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.
