Adaptive offloading inference for delivering applications in pervasive computing environments

Pervasive computing allows a user to access an application on heterogeneous devices continuously and consistently. However, it is challenging to deliver complex applications on, resource-constrained mobile devices, such as cell phones and PDAs. Different approaches, such as application-based or system-based adaptations, have been proposed to address the problem. However, existing solutions often require degrading application fidelity. We believe that this problem can be overcome by dynamically partitioning the application and offloading part of the application execution to a powerful nearby surrogate. This will enable pervasive application delivery to be realized without significant fidelity degradation or expensive application rewriting. Because pervasive computing environments are highly dynamic, the runtime offloading system needs to adapt to both application execution patterns and resource fluctuations. Using the Fuzzy Control model, we have developed an offloading inference engine to adaptively solve two key decision-making problems during runtime offloading: (1) timely triggering of adaptive offloading, and (2) intelligent selection of an application partitioning policy. Extensive trace-driven evaluations show the effectiveness of the offloading inference engine.