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Journal of Software:2020.31(6):1839-1859

基于概率模型检验的云渲染任务调度定量验证
高洪皓,缪淮扣,刘浩宇,许华虎,于芷若
(上海大学 计算中心, 上海 200444;上海大学 计算机工程与科学学院, 上海 200444;上海大学 计算机工程与科学学院, 上海 200444;上海市计算机软件评测重点实验室, 上海 201112;上海大学 计算机工程与科学学院, 上海 200444;上海大学 信息化办公室, 上海 200444)
Applying Probabilistic Model Checking to the Quantitative Verification of Task Scheduling for Cloud Rendering System
GAO Hong-Hao,MIAO Huai-Kou,LIU Hao-Yu,XU Hua-Hu,YU Zhi-Ruo
(Computing Center, Shanghai University, Shanghai 200444, China;School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China;School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;Information Office, Shanghai University, Shanghai 200444, China)
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Received:April 13, 2018    Revised:June 12, 2018
> 中文摘要: 云渲染技术已被广泛应用于影视和动漫等行业.与传统的渲染农场和租赁市场模式不同,云渲染系统依托云计算基础设施提供多种软件服务进行渲染作业的方式,正逐渐成为新兴的计算模式.由于任务执行和资源操作等作业调度对于用户而言是透明的,这要求云渲染系统应具备智能化以实现计算资源优化调度和多端任务管理,并对系统可靠性提出了更高要求.针对这一问题,提出了采用概率模型检验对云渲染系统任务调度进行定量评估.首先,考虑渲染服务失效等因素引发的随机系统异常和指令错误,如文件损坏和渲染任务超时等,提出了基于离散马尔可夫链(DTMC)的概率模型对云渲染系统的文件准备模块、资源请求模块、渲染任务执行模块进行形式化建模;其次,从服务质量属性角度提出了9类验证性质用于定义云渲染系统的可靠性,采用概率计算树逻辑(PCTL)描述检验性质公式并执行工具PRISM计算和验证渲染系统可靠性;最后,结合案例和实验证明了该方法的可行性和有效性,尤其是对改进前后云渲染系统进行定量检验,可用于指导如何进行失效恢复和任务切换.因此,该方法在一定程度上可提高云渲染系统的可靠性.
Abstract:Cloud rendering has been widely used as a new computing architecture for the industries of film, television and animation. However, it is different from traditional methods, such as the render farm and rental market, which can provide a variety of rendering software in the cloud to recede workloads based on cloud infrastructures. In general, task executions and resource operations of task scheduling are transparent to the user. This requires that the cloud rendering system should have the intelligent ability to perform the optimal resources scheduling and multi-terminal tasks management. Thus, the reliability of the cloud rendering system is a core research problem. To this end, the probabilistic model checking technology is employed to carry out the quantitative verification and performance evaluation of the cloud rendering process focusing on task scheduling. First, the rendering service failure will cause stochastic exceptions and instruction errors when cloud rendering is working, i.e., damaged files and task timeout. To this end, the DTMC-based probabilistic model is proposed to formalize the file preparation module, resource request module, and rendering task execution module. Second, considering QoS attributes, nine types of reliability property are introduced to quantitatively verify the cloud rendering system, based on which PCTL is used to describe the verification formula to execute the supporting tool PRISM. Finally, the feasibility and effectiveness of proposed method are demonstrated by case study and experiments, especially the performance of task scheduling can be guaranteed by system recovery and task switching according to the quantitative result generated from formal verifications. Therefore, the proposed method can improve the reliability of the cloud rendering system.
文章编号:     中图分类号:TP391    文献标志码:
基金项目:国家自然科学基金(61502294,61572306);赛尔网络下一代互联网技术创新项目(NGII20170513) 国家自然科学基金(61502294,61572306);赛尔网络下一代互联网技术创新项目(NGII20170513)
Foundation items:National Natural Science Foundation of China (61502294, 61572306); Innovation Project of Next Generation Internet Technology of CERNET (NGII20170513)
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高洪皓,缪淮扣,刘浩宇,许华虎,于芷若.基于概率模型检验的云渲染任务调度定量验证.软件学报,2020,31(6):1839-1859

GAO Hong-Hao,MIAO Huai-Kou,LIU Hao-Yu,XU Hua-Hu,YU Zhi-Ruo.Applying Probabilistic Model Checking to the Quantitative Verification of Task Scheduling for Cloud Rendering System.Journal of Software,2020,31(6):1839-1859