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