Yingying Xiao

New Advances of the Research on Cloud Simulation

Based on the research fruits of Cloud Simulation Platform [1], this paper expounds the latest research results of our team in cloud simulation, including the further research on the technology content and features of cloud simulation, architecture and service patterns of the cloud simulation system, technology system and several improved key technologies ( individuation virtual desktop technology, multi-users oriented dynamic building technology of virtual simulation environment, fault-tolerant migration technology for simulation resources, high performance cloud simulation supported co-simulation platform technology ) of cloud simulation and typical application demonstration system. The primary research and practice show that the proposed latest research results can better support “cloud simulation” pattern, which users can access services of simulation resource and capability on demand anytime and anywhere through network and cloud simulation platform, to accomplish varied activities in the whole simulation life-circle. Finally, this paper gives the prospect of the future work of cloud simulation.

Multi-centric management and optimized allocation of manufacturing resource and capability in cloud manufacturing system

Cloud manufacturing offers the potential to make mass manufacturing resources and capabilities more widely integrated and accessible to users through network. Most related research assumes that there exists only one management center for all manufacturing resources and capabilities in a manufacturing cloud. However, this could cause the efficiency problem (e.g. scheduling time) and harm the quality of service (e.g. response time). Actually, a large-scale manufacturing cloud should have multiple management centers to deal with massive, widely distributed manufacturing resources and capabilities and users; meanwhile, the constraint of finite manufacturing resources and capabilities and the cost of remote collaboration should be taken into consideration. Thus, this article first presents the architecture for the multi-centric management with two-level scheduling strategy combining the advantages of the centralized and decentralized decision-making. Then, after quantifying the availability and the collaborative cost of the manufacturing resources and capabilities, we propose a global optimization model for the manufacturing resources and capability allocation under the multi-centric architecture. Finally, a case study adopting our new method shows that the utilization of the manufacturing resources and capabilities would be more balanced, while the cost of the total collaboration would be reduced, compared with the typical decentralized solution. The research results can support cloud manufacturing to effectively deal with the challenge of management and allocation for increasingly large-scale and distributed manufacturing resources and capabilities.

A cloud simulation based environment for multi-disciplinary collaborative simulation and optimization

Multi-disciplinary virtual prototype (MDVP) based on modeling and computer simulation technology has been applied in a wide range of engineering applications, especially the design, testing and evaluation of complex products. Generally, a large set of parameters need to be optimized to improve the performance of the prototype, which comprises a number of heterogeneous models from multiple domains. This calls for numerous collaborative simulation experiments in heterogeneous computing environments, which are difficult to build and configure. Cloud simulation is a promising solution with the extremely large resource pool and dynamic construction of virtual computing environments. Thus this paper firstly proposed an optimization framework of MDVP which formulated the domain models, optimizer and the distributed interactive environment on cloud for multi-disciplinary engineers. Then we designed the procedure for conducting parallel optimization of MDVP in the cloud simulation system by con-current execution of MDVP simulation system. The method proposed in the paper has been applied to a MDVP in the aerospace industry, which indicates that the proposed method can support an efficient engineering methodology that can transform the traditional centralized, serial simulation optimization to the distributed collaborative and parallel simulation optimization.

A Semantic Composition Framework for Simulation Model Service

In order to solve the large-scale model service composition problem in the cloud of simulation, this paper proposes a simulation model service composition framework which considers the characteristics of the cloud. This simulation model service composition framework adopts an ontology-based simulation model service description strategy (MSDS). Based on MSDS, the composite service composed of several model services with complex topology connection relationships is generated by the Input/Output semantic connection strength and simulation capability. A contrast experiment is conducted for the empirical verification.

Smart Simulation Cloud (Simulation Cloud 2.0)—The Newly Development of Simulation Cloud

A new scientific and technological revolution is emerging around the world and the era of “New Internet + Big Data + Artificial Intelligence+” is coming. As a third approach of understanding and transforming the world after theoretical approach and experimental approach, simulation technology faces major challenge in paradigm, approach, and ecosystem. Based on earlier researches and practices on simulation grid and simulation cloud, our team proposes smart cloud simulation (cloud simulation 2.0) based on our recent years research. The paper introduces the connotation of smart cloud simulation, and the concept model, architecture, body of knowledge, prototype, key technologies and applications of smart simulation cloud (smart cloud simulation system). The innovations of this paper are as follows. We define the connotation of newly cloud simulation the paradigm of which is interconnected, service-oriented, personalized, flexible, sociable and intelligent to satisfy the simulation demands of users at anytime, anywhere. We propose the digitizing, things-connecting, virtualization, service-oriented, collaborating and intelligent enabling technologies respectively. Moreover, typical prototype applications based on high performance simulation computers are proposed to illustrate the availability of our architecture and methods and it works well for the full cycle of simulation.

A Study on the Comparison of “Internet+” Paradigm and Means and the Analysis and Evaluation of Its Typical Development Path

Taking Internet as carrier and adopting emerging information and communication technology, intelligent science and technology, “Internet+” paradigm and means are emerging and applied to the paradigm innovation, business optimization and efficiency improvement. In this paper, comparative analysis is firstly conducted on the “Internet+” paradigm and means from the perspective of paradigm, means and support technology to propose that Smart cloud manufacturing (Smart CMfg) is a scheme to match Chinese national conditions and competitive advantages. From the perspective of model-based system engineering, the incentive and feedback model of manufacturing industry development is then proposed to discuss the development path and mechanism of the transformation and upgrading of Chinese Smart CMfg-based manufacturing industry. Finally, this article demonstrates that the operation of the incentive and feedback model of manufacturing industry development may form massive cloud enterprise network which could support the upgrading and development of Chinese manufacturing industry.

A formulation for IoT-enabled dynamic Service Selection across multiple Manufacturing clouds

Cloud Manufacturing can provide mass manufacturing resources and capabilities as services via the Internet. Undoubtedly, multiple manufacturing clouds (MCs) will have extremely abundant services in terms of function, price, etc. The ability to leverage ample services hosted in MCs has direct relation to the success or failure of a manufacturer. Meanwhile, various uncertainties in todays highly-dynamic business environment can easily disrupt manufacturing activities, rendering original schedules ineffective or even obsolete. IoTs real-time sensing ability can be used to detect those uncertainties. However, little work has been done to take advantage of abundant services from MCs and to effectively deal with uncertainties. In order to address this issue, we propose a mathematical formulation for IoT-enabled dynamic Service Selection (SS) across multiple MCs. We consider three kinds of uncertainties (fluctuation of completion time, choices of manufacturing services, and runtime changes made by users) that come from both the user and market sides. The formulation can guide the dynamic SS and enable users to continuously adjust SS to be more effective and efficient.

A Self-adaptive Shuffled Frog Leaping Algorithm for Multivariable PID Controller’s Optimal Tuning

To insure the multi-input multi-output (MIMO) system has good system response and anti-jamming capability under no decoupling, this paper proposed a self-adaptive shuffled frog leaping algorithm to solve the multivariable PID controller’s optimal tuning problem. First, the mathematical description of optimal tuning problem of multivariable PID controller is given. Second, a modified SFL with a parameter adaptive adjustment strategy in the basis of convergence analysis is proposed to enhance SFL’s global searching ability and to improve its searching efficiency. Finally, a classical simulation example proposed by Wood and Berry is used to compare the performance of our modified SFL with SFL proposed by Thai and wPSO proposed by Shi, and the optimal results of PI/PID controller demonstrate the effectiveness of our algorithm.

Manufacturing Capability Service Modeling, Management and Evaluation for Matching Supply and Demand in Cloud Manufacturing

Currently, the manufacturing sector faces challenges brought by the global technological revolution and industrial revolution. Cloud manufacturing can be of great help to break the traditional pattern of static enterprise resource configuration through the on-demand provision and consumption of manufacturing capability (MCap), and thus optimize industrial chains and improve competitiveness. Comparing to the traditional e-commerce, matching supply and demand of MCap will require the consideration of complex conditions, such as static and dynamic attributes of MCap services, scalability of domain attributes of heterogeneous MCap services, and other characteristics (e.g., multiple stakeholders and multi-round competition) in the open and dynamic environment of social manufacturing. To address the above demand, we propose meta-models for MCap service description, an approach to the management of MCap services based on the data model “EAV” and the enterprise search platform “Solr”, and an approach to the evaluation of MCap services based on the dynamic composition & screening. Finally, those methods have been implemented in a cloud manufacturing platform for an aerospace conglomerate, and the results show their effectiveness.

Modified shuffled frog leaping algorithm for simulation capability scheduling problem

Based on the analysis of characteristics of simulation capability scheduling problem in cloud simulation platform, this paper gives its mathematical description and introduces a modified shuffled frog leaping (MSFL) algorithm to solve the above optimisation problem with multi-mode constraint. The MFSL introduces GA to code the feasible solution space. During the random execution of coding, decoding and mutation, it increases three layers of coding constraints including simulation capability, task logic and feasible mode, to ensure the randomness of the solving process in the controllable scope. Thus, it can reduce the search range of solution space, get rid of the meaningless illegal solution, and ultimately improve the convergence speed of the algorithm and avoid precocity.