Dynamic Distributed Edge Resource Provisioning via Online Learning across Timescales

The strategic management of distributed resources of mobile edge computing networks often requires managing different system components over different timescales. In this paper, we formulate a nonlinear mixed-integer program to capture the online optimization of the edge network's long-term cost, where we distribute workload more frequently on the fast timescale and provision resources less frequently on the slow timescale. We design a novel online learning framework consisting of three algorithms to make fast-timescale and slow-timescale fractional decisions, respectively, and round such decisions into integers. Our algorithms run in polynomial time in an online manner, jointly solving the original NP-hard problem that can contain arbitrary and unpredictable inputs. Via rigorous formal analysis, we prove a parameterized-constant competitive ratio as the performance guarantee for our approach. We conduct extensive evaluations with real-world data and confirm our approach's superiority over existing practices and state-of-the-arts.