System-wide profiling and optimization with virtual machines

Abstract

Simulation is a common approach for assisting system design and optimization. For system-wide optimization, energy and computational resources are often the two most critical limitations. Modeling energy-states of each hardware component and time spent in each state is needed for accurate energy and performance prediction. Tracking software execution in a realistic operating environment with properly modeled input/output is key to accurate prediction. However, the conventional approaches can have difficulties in practice. First, for a complex system such as an Android smartphone, building a cycle-accurate simulation environment is no easy task. Secondly, for I/O-intensive applications, a slow simulation would significantly alter the application behavior and change its performance profile. Thirdly, conventional software profiling tools generally do not work on simulators, which makes it difficult for performance analysis of complicated software, e.g., Java applications executed by the Dalvik virtual machine. Recently, virtual machine technologies are widely used to emulate a variety of computer systems. While virtual machines do not model the hardware components in the emulated system, we can ease the effort of building a simulation environment by leveraging the infrastructure of virtual machines and adding performance and power models. Moreover, multiple sets of the performance and energy models can be selectively used to verify if the speed of the simulated system impacts the software behavior. Finally, performance monitoring facilities can be integrated to work with profiling tools. We believe this approach should help overcome the aforementioned difficulties. We have prototyped a framework and our case studies showed that the information provided by our tools are useful for software optimization and system design for Android smartphones.