Learn and Pick Right Nodes to Offload
This work addresses efficient computational resource utilization in fog computing for applications requiring low-latency task offloading, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of minimizing long-term latency in fog computing task offloading by formulating a stochastic programming problem with abrupt changes in system parameters and proposing an algorithm under a non-stationary bandit model, achieving asymptotic optimality as proven and corroborated through numerical simulations.
Task offloading is a promising technology to exploit the benefits of fog computing. An effective task offloading strategy is needed to utilize the computational resources efficiently. In this paper, we endeavor to seek an online task offloading strategy to minimize the long-term latency. In particular, we formulate a stochastic programming problem, where the expectations of the system parameters change abruptly at unknown time instants. Meanwhile, we consider the fact that the queried nodes can only feed back the processing results after finishing the tasks. We then put forward an effective algorithm to solve this challenging stochastic programming under the non-stationary bandit model. We further prove that our proposed algorithm is asymptotically optimal in a non-stationary fog-enabled network. Numerical simulations are carried out to corroborate our designs.