Affiliation:
1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, 071003 Hebei, China
Abstract
Mobile edge computing (MEC) nodes are deployed at positions close to users to address excessive latency and converging flows. Nevertheless, the distributed deployment of MEC nodes and offload of computational tasks among several nodes consume additional energy. Accordingly, how to reduce the energy consumption of edge computing networks while satisfying latency and quality of service (QoS) demands has become an important challenge that hinders the application of MEC. This paper built a local-edge-cloud edge computing network and proposes a multinode collaborative computing offloading algorithm. It can be applied to smart homes, realize the development of green channels, and support local users of Internet of Things (IoT) to decompose computational tasks and offload them to multiple MEC or cloud nodes. The simulation analysis reveals that the new local-edge-cloud edge computing offload method not only reduces network energy consumption more effectively compared with traditional computing offload methods but also ensures the implementation of more data samples.
Funder
Fundamental Research Funds for the Central Universities
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
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