Crowdsourcing the cloud: energy-aware computational offloading for pervasive community-based cloud computing

Adaptive offloading systems achieve context specific optimization on mobile and pervasive devices by offloading computational components to a resource copious remote server or cloud. However, with the recent advancement in computational capacity of mobile and pervasive devices, adaptive offloading could facilitate the formation of ad-hoc cloud-like environments using collections of mobile and pervasive devices, with reduced reliance on centralized infrastructure. Therefore, in this paper, we formulate a decision-making strategy for global adaptive offloading that distributes application components to community-based clouds formed from multiple collaborating peers. The goal was to extend the collaboration and application lifetime by optimizing the Time to Failure (TTF) of devices due to energy depletion, while meeting application specific performance constraints. Specifically, a max-min technique was used to maximise the minimum TTF in order to balance energy consumption across collaborating devices. The efficacy, performance and scalability of the formulated model were evaluated with the proposed algorithm producing an optimal solution to the specified model, using integer linear programming, in affordable time and energy for a range of application and collaboration sizes