Reducing Task Completion Time in Mobile Offloading Systems through Online Adaptive Local Restart

Abstract

Offloading is an advanced technique to improve the performance of mobile devices. In a mobile offloading system, heavy computations are migrated from resource constrained mobile devices to powerful cloud servers through a wireless network connection. The unreliable wireless network often disturbs system operation. Task completion can be delayed or interrupted by congestion or packet loss in the network. To deal with this problem the offloaded jobs can be locally restarted and completed in the mobile device itself.In this paper, we propose a dynamic scheme to determine whether and when to locally restart a task. First, we design an experiment to explore the impact of packet loss and delay in unreliable networks on the completion time of an offloading task. Then, we mathematically derive the prerequisites for local restart and selection of the optimal timeout. The analysis result confirms that local restart is beneficial when the distribution of task completion time has high variance. Further, a dynamic local restart scheme is proposed for mobile applications. This scheme keeps track of the variance of the probability density function of the distribution of task completion time. This is done using a dynamic histogram, which collects and updates data at run time. The efficiency of the local restart scheme is confirmed by experimental results. The experiment shows that local restart at the right time achieves better performance than always offloading.