[ACM]
Mobile devices have less computational power and poorer Internet connections than other computers. Computation offload, in which some portions of an application are migrated to a server, has been proposed as one way to remedy this deficiency. Yet, partition-based offload is challenging because it requires applications to accurately predict whether mobile or remote computation will be faster, and it requires that the computation be large enough to overcome the cost of shipping state to and from the server. Further, offload does not currently benefit network-intensive applications.
In this paper, we introduce Tango, a new method for using a remote server to accelerate mobile applications. Tango replicates the application and executes it on both the client and the server. Since either the client or the server execution may be faster during different phases of the application, Tango allows either replica to lead the execution. Tango attempts to reduces user-perceived application latency by predicting which replica will be faster and allowing it to lead execution and display output, leveraging the better network and computation resources of the server when the application can benefit from it. It uses techniques inspired by deterministic replay to keep the two replicas in sync, and it uses flip-flop replication to allow leadership to float between replicas. Tango currently works for several unmodified Android applications. In our results, two computation-heavy applications obtain up to 2-3x speedup, and five network applications obtain from 0 to 2.6x speedup.