Research On Issues Of Dynamic Process Optimization Scheduling Under Cloud Computing Environment

Nearly two years, the demand of large scale instance intensive workflow application is increasing, which provides workflow technology for more vast demand space. In the manufacturing, some workflow instances are not only large scale and instance intensive, and still, there is dependence between them. This makes the current workflow system cannot meet the needs of modem enterprise. Cloud workflow provides the technology means to solve the problem of large scale workflow application. On the one hand, a lot of cloud services can meet the requirement of workflow execution, and satisfy the needs of users. On the other hand, it makes the organization of many cloud services through workflow. Users can custom their business process through the virtual modeling tools.Workflow service in the cloud computing environment is the same as other services. It provides service through service customized way too. All services must follow the market oriented mode, and pay for use. Workflow service is no exception. At workflow initial stage, users firstly sign the contract in cloud computing platform, submit the definition of workflow, search and match cloud services, and then bind the process definition. At workflow execution stage, workflow engine will adopt the optimized scheduling strategy according to user's QoS. If QoS conflict is appeared, workflow engine will process the conflict according to the configuration in advance.In this processing, the workflow running environment has been large changed. The requirements of user's non-function, such as performance, efficiency, as well as safety are higher. And the requirements of user's personalized QoS service customization must be satisfied and supported by the cloud workflow platform in the lifecycle. Therefore, in order to adapt this change, some typical problems of workflow must be put forward, such as architecture, scheduling, resource management, etc.The research of key issues under cloud workflow platform is supported by the national863high and new project "The collaborative service platform funded for equipment manufacturing industry cluster" and "The information service platform for business associated small and medium sized enterprises", etc. Based on the functions of Shandong manufacturing industry information service platform, it extends more functions and satisfies the needs of personalized QoS customization. In the background of manufacturing industry, some key issues such as cloud workflow architecture, lifecycle, composition process, task scheduling and resource management are discussed.The main contributions of the thesis are as follows:1. Propose the cloud workflow system architcture of instance intensive application orientedCloud workflow system in the workflow system platform not only uses the infrastructure services provided by cloud computing environment, but also collaborate the other services which is provided by other service providers. Therefore, this paper maps the cloud workflow system architecture into cloud computing platform, and put forward cloud workflow system architecture. Then, we put forward the four processes of cloud workflow lifecycle, including modeling and simulation process in workflow initial stage, searching, matching, scheduling and executing process in workflow execution stage, and trading and evaluation process in workflow complete stage.2. Propose the service collaborative model based on dynamic composition process.Cloud workflow engine will be responsible for quickly searching the services and scheduling those services to the service resources that provided by cloud service providers. Because cloud computing environment service provider's services are heterogeneous, it needs to provide a uniform encapsulation, resource selection and binding standards. Therefore, we put forward the method of dynamic service composition process, including business function modeling, service searching and matching, service dynamic binding. According to the relationship of service dependence, we put forward the dependency validation algorithm.3. Propose the QoS based global optimized task scheduling model and algorithm.Due to adopting the pay it on demand mode, users in cloud computing environment have high needs of QoS. The QoS aware workflow scheduling method includes the conditions of the QoS constraints. In the service layer, it uses improved genetic algorithm as pre-scheduling method to improve the throughput in workflow execution stage. 4. Propose load-aware based resource management and delayed scheduling strategy and algorithmIn order to ensure the enough resource in workflow execution stage and improve the peak load capability of the platform. In the task layer, it puts forward load-aware based delayed scheduling strategy and focuses on the problems of scheduling chance and present the delayed scheduling algorithm.On the above research works, we design and develop cloud workflow system prototype (I2-CWS) based on Shandong manufacturing industry information service platform (SDMSP), and discuss the application cases. In this paper, we only solve some parts of problems in cloud workflow platform. Other problems such as workflow change and interoperability will be as the further research works.