Bandwidth-adaptive application partitioning for execution time and energy optimization

Partitioning and offloading some parts of mobile applications onto remote servers is a promising approach to extend the battery life of mobile devices. However, since available network bandwidths vary in a wireless environment, static partitionings proposed by previous works with a fixed bandwidth assumption are unsuitable for mobile platforms, while dynamic partitionings result in high overhead due to continuously partitioning. Targeting this problem, we propose a novel partitioning scheme taking the bandwidth as a variable to improve static partitioning and avoid high costs of dynamical partitioning. Based on the application Object Relation Graph, we propose a partitioning optimization model and two bandwidth-adaptive partitioning algorithms: Branch-and-Bound based Application Partitioning (BBAP) and Min-Cut based Greedy Application Partitioning (MCGAP). BBAP is suitable for obtaining the optimal partitionings for small applications, while MCGAP is applicable to large-scale applications by quickly obtaining suboptimal solutions. Experimental results demonstrate that both algorithms can adapt to bandwidth fluctuations well, and significantly reduce the execution time and energy consumption by optimally distributing components between mobile devices and servers.