PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

摘要

Fog computing is a promising paradigm that enables Internet-of-Things (IoT) applications with ultra-low latency and intensive computation. However, it is challenging to make efficient online decisions under varying system dynamics and intertwined power-latency tradeoffs. Moreover, the fundamental limits and benefits of predictive offloading in fog computing systems still remain unknown. In this paper, we study the problem of dynamic workload offloading and resource allocation in multi-tiered fog computing systems. By developing a fine-grained queue model and formulate a stochastic network optimization problem, we propose PORA, an efficient scheme that exploits predictive information to solve the problem. Results from our theoretical analysis and simulations show that PORA achieves a near-optimal power consumption with low latencies. Furthermore, PORA effectively reduces latencies with only mild-value of predictive information and it's robust against prediction errors.