A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper,we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment.In general,the different slices for different task scenarios exist in the same edge layer synchronously.A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity.In the condition,the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment.Based on the slicing and container concept,we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme(TDACP).The scheme divides the resource allocation and management work into three stages in this paper:In the first stage,it designs reasonably strategy to allocate resources to different task slices according to demand.In the second stage,it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement(SLA)of the virtual machine in different slices.In the third stage,it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers.Thus,it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost.The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy.It adjusts the number of equivalent virtual machines based on the SLA range of system parameter,and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear.The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.

Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.

A comprehensive framework for assessing the spatial drivers of flood disasters using an Optimal Parameter-based Geographical Detector-machine learning coupled model

Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.