Developing Energy-Aware Task Allocation Schemes in Cloud-Assisted Mobile Workflows

Mobile cloud computing is an emerging field of research which aims to provide a platform on which intelligent and feature-rich applications are delivered to the user at any time and at anywhere. When such a cloud-assisted mobile application workflow requires the cooperation of many devices, solving the task allocation problem becomes a critical step in ensuring the energy efficiency of the mobile cloud platform. In this paper, we construct a quadratic binary program to model the task allocation problem in such scenarios. In order to overcome the poor scalability of generic quadratic program solvers, we present an implementation of the simulated annealing algorithm and a greedy autonomous offload algorithm to approximate the optimal solution. Both heuristics are tailored to solve our task allocation problem efficiently. We verify and compare our algorithms against a commercial quadratic program solver in a series of simulations. Results show that both heuristics produce good solutions to the task allocation problem. Solutions provided by our greedy algorithms is consistently close to optimal and can be obtained in a more time efficient manor than our implementation of the simulated annealing algorithm.