Xudong Chai

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.

Research on key technologies of complex product virtual prototype lifecycle management (CPVPLM)

Abstract As a new product design and development method, virtual prototyping is the advanced research in the modeling and simulation (M&S) field. Nowadays virtual prototyping has evolved into a virtual prototype engineering. Considering that PDM/PLM systems are unable to manage the characteristics of models/content/knowledge of complex product virtual prototype (CPVP) and lifecycle of CPVP, this paper gives the concept of complex product virtual prototype lifecycle management (CPVPLM). Then, the author presents and discusses a three-dimensional (3D) view to reinforce the share and management of the CPVP lifecycle information. Moreover, the paper gives and studies a framework to solve the lifecycle management of CPVP. To manage the digital content in CPVPLM, the content management technology is introduced. To add semantic description information into the documents and models, the ontology technology is introduced. Finally the article introduces the use of CPVPLM prototype system, and gives an illustrative application example.

The Triangular Pyramid Scheduling Model and algorithm for PDES in Grid

Abstract Grid is a perfect environment for the large scale Parallel Discrete Event Simulation (PDES), because its distribution and collaboration features match the PDES applications well. The PDES tasks or applications are modeled as a Directed Acyclic Graph (DAG), in which the simulation resources consist of three critical factors, simulation hosting machine (SHM), simulation service (SS) and simulation data (SD) in Grid environment. By solving the model we attempt to obtain an optimized triangular matching of the simulation resources on Grid, so that it can support the PDES activities better. We name the algorithm of solving the model Triangular Pyramid Scheduling (TPS). The PDES DAG is divided into three basic graph structures: Sequential structure, Fork structure, and Join structure. The TPS algorithm is developed based on these graph structures. The simulation results show that TPS algorithm can reduce the makespan and congestion, improve the simulation efficiency, and increase the resource utilization efficiency, compared to the existing algorithms.

GDSA: A Grid-Based Distributed Simulation Architecture

This paper focuses on architecture suitable for large-scale simulation system. Based on the scenario of large-scale Internet simulation, the grid technology is introduced and a new architecture, GDSA is proposed. GDSA, a full grid-based architecture mainly focuses on four pending problems in distributed system: scalability, communications, management mechanism and QoS insurance computing environment. The advantages of grid in scalability and uniform communication will be used to improve systems scalability and communication method. Management system will be built according to meta-service mechanism. Contractual computing mechanism (CCM), a special mechanism added in GDSA, will provide QoS insurance for users. Three-layer QoS model, which is the core of CCM, will take charge of QoS problems in different levels. At the end, a prototype is designed and the experiment results based on this platform show that GDSA is a promising system to overcome the pending problems in distributed simulation

QoS Enhancement for PDES Grid Based on Time Series Prediction

The combination of parallel/distributed discrete event simulation (PDES) and Grid technology is a new trend in simulation. QoS is important yet difficult in this process. With the special features of Gird architecture and PDES input, such as periodical and predictable inputs, we could enhance QoS with a PDES-specific prediction. A Grid-based framework is presented that is designed to help predict the performance of PDES. Based on this framework, a prediction algorithm using time series theory is presented in the context of large scale Grid simulations. Experiments are executed in the context of GridSim, which shows methods discussed before to help to improve QoS level.

On an Automatic Simulation Environment Customizing Services for Cloud Simulation Center

Simulation plays an important role in both academic research and industrial development and manufactory. The users’ requirements for simulation are often complex and diverse. It is time consuming and error-prone for users to build different simulation environments manually to conduct their simulation tasks. An Automatic Simulation Environment Customiz¬ing Service for Cloud Simulation Center is presented in this paper to address this problem. The service offers an automatic simulation environment that can customize and configure service requirements of simulation users with high efficiency, flexibility and agility. The system is capable of processing diversified requests dynamically and responding the requests in real-time. The operation and experiments on the prototype system operation and experiment show higher availability, flexibility, and reusability of our method than the traditional simulation environment customizing method.

Study on Virtualization-Based Simulation Grid

Firstly, the paper concisely introduces the research background of a new kind of networked modeling and simulation (M&S) platform based on the virtualization technology namely Virtualization-based Simulation Grid. Then its connotation and the architecture of platform prototype are presented. Furthermore, some solved key techniques and some typical applications developed by authors are introduced, such as simulation resource virtualization technique, then simulation resource discovery and scheduling technique based on virtualization etc. It can strengthen the capabilities of current networked M&S platform on dynamical resources sharing on-demand, supporting multi-user, collaboration, fault tolerant and security mechanism thus anew modeling and simulation mode is established. Finally, the conclusion and some further works are given.

Research and Applications of Cloud Manufacturing in China

This chapter leads to the achievements of research and applications in Cloud Manufacturing (CMfg) carried out by the authors’ team. First of all, the meaning of Big Manufacturing is given, and the challenges and strategies for manufacturing industries as well as the content and development of manufacturing informatization are analyzed. Then the definition, concept model, system architecture, technologies, typical technical characteristics, and service object and content of CMfg are put forward. Moreover, discussions are shown to prove that CMfg is a new paradigm and approach to realize manufacturing informatization, which materializes and extends Cloud Computing in the manufacturing domain. The current status of the technologies, applications, and industries for CMfg in China are presented. Concerns are mostly focused on the 12 key technologies for the CMfg System (also called Manufacturing Cloud), including system overall technology, sensing technology for manufacturing resource and capability, virtualization and service technology for manufacturing resource and capability, construction and management technology for virtual manufacturing environment, operation technology for virtual manufacture environment, evaluation technology for virtual manufacturing environment, trusted service technology for virtual manufacturing, management of knowledge, model and data, pervasive human–computer interaction technology, application technology of service platform, informatized manufacturing technology system, and product service technology. The academic research results of the authors’ team are presented. Furthermore, four typical CMfg cases which have been successfully implemented in group enterprises and mid-small enterprise clusters are described as well as cases introducing smart manufacturing into smart city. Finally, future works and development prospect for CMfg are raised.

A simulation cloud monitoring framework and its evaluation model

Abstract Simulation Cloud can help users to carry out the simulation tasks in various stages quickly and easily by renting instead of buying all the needed resources, such as the computing hardware, simulation devices, software, and models. A monitoring system is necessary, which can dynamically collect information about the characteristics and status of resources in real time. In this paper, we design a Simulation Cloud Monitoring Framework (SCMF), which is a Monitoring Framework based on Simulation Cloud. The main functions of SCMF include: 1. Collecting performance information of Simulation Cloud (including physical resources and virtual resources). 2. Processing the collected performance information, providing ranking information about resource consumption as the customized service to service layer. 3. Detecting abnormal behaviors on Simulation Cloud in real time. The SCMF is based on hierarchical design. It consists of Root Monitoring Node (RMN), Federation Monitoring Node (RMN), and Main Monitoring Node (MMN). There is only one RMN in SCMF. It is responsible for collecting metadata about Simulation Cloud. For robustness, there are several FMNs in a federation. One is primary FMN and others are backup FMNs. MMN is implementing on every host in Simulation Cloud., MMN is responsible for collecting performance information about the host and virtual nodes. In the paper, it designs Sequence-Bucket strategy, which supports quick response for ranking information about resource consumption. It also designs two strategies: Rank-FMN (Federation Monitor Node) strategy and Huffman-Like Strategy. Huffman-Like Strategy combines small federations to reduce total consumption of SCMF, while Rank-FMN strategy is a load balancing strategy, which relieves the bottleneck of FMNs and spreads the loads equally among FMNs. The characteristics of SCMF are real-time, scalability, robustness, light weight, manageability, and archivability. Meanwhile, we design evaluation models for SCMF, which can provide quantitative results of monitoring accuracy and monitoring cost. The simulation results show that SCMF is accurate, low cost and can response in real-time.

Research and application on ontology-based layered cloud simulation service description framework

Cloud simulation system improves the ability of current network-based M&S in on-demand simulation and massive-user service. The share of multi-granularity resources and dynamic establishment of simulation services in Cloud Simulation raise new challenges to the Simulation Service Description Framework (SSDF). An ontology-based layered SSDF (OLSSDF) was proposed towards those challenges in cloud simulation, which includes the layered architecture of cloud simulation service and the ontology semantics of each layer in the framework which were defined and formalized in OWL-S. The OLSSDF for cloud simulation was applied and in the description of simulation services in certain cloud simulation system prototype of aero plane. The primary research and application show that the OLSSDF oriented to cloud simulation, which describes cloud simulation services in both attribute-semantic and model-semantic, adapts well to the various multi-granularity simulation resources and facilitates the intelligent discovery and automatic combination of simulation services in the cloud simulation mode.