An energy-efficient offloading framework with predictable temporal correctness

As battery-powered embedded devices have limited computational capacity, computation offloading becomes a promising solution that selectively migrates computations to powerful remote severs. The driving problem that motivates this work is to leverage remote resources to facilitate the development of mobile augmented reality (AR) systems. Due to the (soft) timing predictability requirements of many AR-based computations (e.g., object recognition tasks require bounded response times), it is challenging to develop an offloading framework that jointly optimizes the two (somewhat conflicting) goals of achieving timing predictability and energy efficiency.