Dynamic Distributed Edge Resource Provisioning via Online Learning across Timescales
2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)(2020)
摘要
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.
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关键词
mobile edge computing networks,system components,nonlinear mixed-integer program,online optimization,novel online learning framework,slow-timescale fractional decisions,original NP-hard problem,dynamic distributed edge resource provisioning,strategic management,fast-time scale fractional decisions,edge network long-term cost,polynomial time,rigorous formal analysis,parameterized-constant competitive ratio
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