Abstract
With the continuous integration of cloud computing, edge computing, and Internet of things (IoT), various mobile applications will emerge in future 6G network. Driven by real-time response and low energy consumption requirements, mobile edge-cloud computing (MECC) will play an important role to improve user experience and reduce costs. However, due to the complexity of applications, the computing capacity of devices cannot meet the low-latency and low energy consumption requirement. Meanwhile, subject to the limited supplement of power and energy system, the heterogeneous multilayer mobile edge-cloud computing (HetMECC) is proposed to join cloud server, edge server, and terminal devices for data calculation and transmission. By dividing computing tasks, terminal applications can receive reliable and efficient computing services. The simulation results show that the proposed model can achieve the low-latency requirement of data calculation and transmission and improve the robustness of architecture.
Highlights
Introduction e5G communication technology has three important application prospects: enhanced mobile broadband (EMB), massive machine-type communication (EMTC), and ultrareliable and low latency communication (URLLC) [1].EMTC can exchange information based on large-scale data between machines without human intervention
Experimental Results and Analysis. e task offloading strategy task offloading model optimization (TOMO) algorithm is compared with local execution (LE), edge server execution (EC), and cloud computing center execution (CC) from aspects of end device energy consumption, task completion, system latency, fitness value, and robustness analysis. en, the comparison result is obtained
When the number of Single layer data generation rate (×60Kbytes) published tasks is 50 in each layer, the completion time of heterogeneous multilayer mobile edge-cloud computing (HetMECC) is lower than EC
Summary
An analytical methodology is proposed to analyze system latency of the HetMECC model, as shown in Figure 1. e dynamic analysis process is shown in Figure 2: first of all, the raw data are obtained through the monitoring of the IOT technology in application scenarios. The task offloading model of HetMECC network processes the input dynamic raw data through computation of the system latency method, computation of energy consumption method, and fitness computing method. It can process the raw data generated by itself and transfer computation result to cloud server. In the HetMECC model, the system latency consisted of computation time, raw data transmission time, and temporary results receiving/sending time. Tasks were offloaded to edge devices or cloud computing servers when the value is 1. The sum of F(i) should be larger in the task offloading model. is paper proposed a fitness function to evaluate the task offloading model under the time constraint [10, 15] as follows: fitness p1 ∗ Esum + p2 ∗ 10 ∗ Esum ∗ Tsum/Tc, (10) Fsum where p1 and p2 denote device type parameters under determined task offloading strategy, Esum denotes the total energy consumption, Tsum denotes the total system latency, and Tc denotes the time constraint
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