Towards Big data processing in IoT: network management for online edge data processing
This work addresses resource management for big data processing in IoT, but it appears incremental as it builds on existing MEC frameworks with a specific optimization approach.
The paper tackles the problem of heavy data loads and wide coverage in IoT by proposing a network management algorithm for mobile-edge computing that jointly optimizes edge processor frequency, transmission power, and bandwidth allocation to stabilize data processing delay and save energy, without requiring knowledge of data source probability distributions.
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.