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.
Highlights
With the continuous development of the Internet of Things (IoT) technology in recent years, IoT network equipment has developed perception and communication abilities, and the user end of the network can extend to information exchange and communication between any goods in daily life [1]
For the multinode collaborative offload model, three mobile edge computing (MEC) nodes are set, and ρ0, ρ1, ρ2, ρ3, ρ4 represent the number of user data units at the local user ends of IoT, the three MEC nodes, and the cloud nodes, respectively
The three MEC nodes correspond to different CPU parameters, and their distances from the local user ends of IoT are denoted by d1, d2, d3, respectively, assuming that the network transmission bandwidth meets user demand
Summary
With the continuous development of the Internet of Things (IoT) technology in recent years, IoT network equipment has developed perception and communication abilities, and the user end of the network can extend to information exchange and communication between any goods in daily life [1]. To solve the network energy consumption problem under a large data size at the user ends of IoT, this study initially analyzes and selects MEC nodes in a local-edge-cloud edge computing network model while considering the distances between the MEC nodes and user ends, the channel characteristics, and the CPU energy consumption. An integral linear programming problem that targets the optimization of network energy consumption is formulated, and single-user task offloading is analyzed by using the branch-and-bound (BB) algorithm to minimize the overall network energy consumption (3) The simulation results show that the demands for MEC nodes increase along with the size of offload data at the local user ends of IoT.
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