  Heavy data load and wide cover range have always been crucial problems for
internet of things (IoT). However, in mobile-edge computing (MEC) network, the
huge data can be partly processed at the edge. In this paper, a MEC-based big
data analysis network is discussed. The raw data generated by distributed
network terminals are collected and processed by edge servers. The edge servers
split out a large sum of redundant data and transmit extracted information to
the center cloud for further analysis. However, for consideration of limited
edge computation ability, part of the raw data in huge data sources may be
directly transmitted to the cloud. To manage limited resources online, we
propose an algorithm based on Lyapunov optimization to jointly optimize the
policy of edge processor frequency, transmission power and bandwidth
allocation. The algorithm aims at stabilizing data processing delay and saving
energy without knowing probability distributions of data sources. The proposed
network management algorithm may contribute to big data processing in future
IoT.
