LAUSR

Computation offloading is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capability of mobile devices. In this paper, we study the task offloading and resource purchasing problems in an edge-cloud collaborative system. The purpose of this system is to minimize the cost of task offloading while ensuring that the tasks can be served before their maximum acceptable delays. Due to the uncertainty of both the task arrival rates and the prices of the computing resources, it is impossible to make an optimal decision online for a long-running time. Therefore, we propose a two-timescale Lyapunov optimization algorithm to overcome the uncertainty of the system's future information and make the optimal decisions only based on the system's current states. By purchasing computation resources in different timescales from the public cloud and making online decisions on where and how many requests should be offloaded, we can achieve an efficient outcome such that the system performance can approach the offline optimum without requiring a priori knowledge of system statistics. Rigorous theoretical analysis confirms the effectiveness of the proposed two-timescale Lyapunov optimization algorithm and extensive trace-driven experimental results show that the algorithm achieves outstanding performance gains over existing benchmarks.