Your friends are more powerful than you: Efficient task offloading through social contacts

In this work, we investigate the distributed and balanced task reassignment in mobile social networks. Previous studies have shown the power of random choice in load balancing with random walking model. Inspired by the `2-choice´ paradigm in `ball and bin´ theory, we evaluate this simple but effective scheme with real trace data `MobiClique´. According to the preliminary evaluation results, we find that, social relationship significantly differs from pure random walk model, and will bring challenges in task reassignment in the followings: First, friendships are relatively stable, which will lead to imbalanced task assignment. Second, some users meet quite infrequently, which will lead to intolerable time delay and uneven task distribution. In tackling with these challenges, we propose `iTop-K´, leveraging the basic concept, i.e., your friends are more powerful than you, which encourages mobile users to assign tasks among intimate friends instead of pure random assignment. With the selection of `top-K´ friends, we can achieve load balancing and guaranteed network performance at the same time. Experimental studies verify our scheme and show the effectiveness. In typical working scenario, where real-trace driven simulation is applied, ours outperforms the conventional random choice up to 15×, and the social relationship assignment without priority method up to 9×.