期刊名称：International Journal of Networking and Computing
印刷版ISSN：2185-2847
出版年度：2016
卷号：6
期号：1
页码：124-147
语种：English
出版社：International Journal of Networking and Computing
摘要：Performance analysis and troubleshooting of cloud applications are challenging. In particular, identifying the root causes of performance problems is quite difficult. This is because profiling tools based on processor performance counters do not yet work well for an entire virtualized environment, which is the underlying infrastructure in cloud computing. In this work, we explore an approach for unified performance profiling of an entire virtualized environment by sampling only at the virtual machine monitor (VMM) level and applying common-time-based analysis across the entire virtualized environment from a VMM to all guests on a host machine. Our approach involves three parts, each with novel techniques: centralized data sampling at the VMM-level, generation of symbol maps for programs running in both guests and a VMM, and unified analysis of the entire virtualized environment with common time by the host-time-axis. We also describe the design of unified profiling for an entire virtual machine (VM) environment, and we actually implement a unified VM profiler based on hardware performance counters. Finally, our results demonstrate accurate profiling. In addition, we achieved a lower overhead than in a previous study as a result of having no additional context switches by the virtual interrupt injections into the guests during measurement.Â
其他摘要：Performance analysis and troubleshooting of cloud applications are challenging. In particular, identifying the root causes of performance problems is quite difficult. This is because profiling tools based on processor performance counters do not yet work well for an entire virtualized environment, which is the underlying infrastructure in cloud computing. In this work, we explore an approach for unified performance profiling of an entire virtualized environment by sampling only at the virtual machine monitor (VMM) level and applying common-time-based analysis across the entire virtualized environment from a VMM to all guests on a host machine. Our approach involves three parts, each with novel techniques: centralized data sampling at the VMM-level, generation of symbol maps for programs running in both guests and a VMM, and unified analysis of the entire virtualized environment with common time by the host-time-axis. We also describe the design of unified profiling for an entire virtual machine (VM) environment, and we actually implement a unified VM profiler based on hardware performance counters. Finally, our results demonstrate accurate profiling. In addition, we achieved a lower overhead than in a previous study as a result of having no additional context switches by the virtual interrupt injections into the guests during measurement.Â
