Multi-QoS Guaranteed Resource Allocation for Multi-Services Based on Opportunity Costs

To meet the booming development of diversified services and new applications in the future, the fifth-generation mobile conmmnication system （5G） has arisen. Resources are increasingly scarce in the @namic time-varying of 5G networks. Allocating resources effectively and ensuring quality of service （QoS） requirements of multi-seiwices come to be a research focus. In this paper, we utilize effective capacity to build a utility function with multi-QoS metrics, including rate, delay bound and packet loss ratio. Taking advantage of opportunity cost （OC）, we also propose a multi-QoS guaranteed resource allocation algm＇ithm for multi-services to consider the future condition of system. In the algorithm, according to different business characteristics and the theory of OC, we propose different selection conditions for QoS users and best effort （BE） users to choose more reasonable resources. Finally, simulation results show that our proposed algorithm achieves superior system utility and relatively better fairness in multi-service scenarios.

Adaptive Learning-Based Delay-Sensitive and Secure Edge-End Collaboration for Multi-Mode Low-Carbon Power IoT

Multi-mode power internet of things(PIoT)combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park.Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping.However,edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service(QoS)guarantee,inadaptability of resource management,and multi-mode access conflict.We propose an Adaptive learning based delAysensitive and seCure Edge-End Collaboration algorithm(ACE_(2))to optimize multi-mode channel selection and split device power into artificial noise(AN)transmission and data transmission for secure data delivery.ACE_(2) can achieve multi-attribute QoS guarantee,adaptive resource management and security enhancement,and access conflict elimination with the combined power of deep actor-critic(DAC),“win or learn fast(WoLF)”mechanism,and edge-end collaboration.Simulations demonstrate its superior performance in queuing delay,energy consumption,secrecy capacity,and adaptability to differentiated low-carbon services.

FogMed:A Fog-Based Framework for Disease Prognosis Based Medical Sensor

Recently,an increasing number of works start investigating the combination of fog computing and electronic health(ehealth)applications.However,there are still numerous unresolved issues worth to be explored.For instance,there is a lack of investigation on the disease prediction in fog environment and only limited studies show,how the Quality of Service(QoS)levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance(e.g.,accuracy and time efficiency).To address these issues,we propose a fog-based framework for disease prediction based on Medical sensor data streams,named FogMed.This framework aims to improve the disease prediction accuracy by achieving two objectives:QoS guarantee of fog services and anomaly prediction of Medical data streams.We build a virtual FogMed environment and conduct comprehensive experiments on the public ECG dataset to validate the performance of FogMed.The experiment results show that it performs better than the cloud computing model for processing tasks with different complexities in terms of time efficiency.

Context-aware end-to-end QoS diagnosis and quantitative guarantee based on Bayesian network

To support quality of service （QoS） management on current Internet working with best effort,we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis,we take contexts of a service into consideration in a comprehensive way that is realized by exploiting causal relationships between a QoS metric and its contexts with the help of Bayesian network （BN） structure learning. Context discretization algorithm and node ordering algorithm are proposed to facilitate BN structure learning. The QoS metric is diagnosed to be causally related to its causal contexts,and the QoS metric can be quantitatively guaranteed by its causal contexts. For quantitative QoS guarantee,those causal relationships are first modeled quantitatively by BN parameter learning. Then,the QoS metric is guaranteed to certain value with a probability given its causal contexts tuned to suitable values,that is,quantitative QoS guarantee is reached. Simulations with three sequential stages：context discretization,QoS diagnosis and quantitative QoS guarantee,on a peer-to-peer （P2P） network,are discussed and our approach is validated to be effective.