Profile-guided Java program partitioning for power aware computing

Summary form only given. In embedded devices, like PDAs, limited battery power presents a bottleneck while running power-hungry applications. Hence, it is important for such applications to be energy aware. Consider a scenario where the embedded device is wirelessly linked to a tethered server and can exploit the computing cycles in it. This presents an opportunity to execute parts of an application remotely and reduce computing energy on the embedded device. However, such a scheme is limited by the transmission energy needed in transferring program parts to be executed remotely. We present a fast profile-guided partitioning algorithm that can choose the program parts that can be executed remotely and save energy on the embedded device. We argue that the partitioning might have to be done on the client device more than once and hence the need for a efficient algorithm. We have chosen Java programs in particular because of their widespread use in embedded applications. Experiments show that our approach can conserve energy in embedded devices from 29% to 43%.