Quantitative evaluation modeling of mobile Ad Hoc networkcommunication service availability under failure|

The availability of network communication services is an important evaluation standard to measure the ability of the network to meet the user’s business needs.The current related research mostly focuses on the evaluation of network reliability,which cannot reflect the logical relationship between failure/maintenance and network performance parameters.Mobile Ad Hoc Network is a multi-hop,centerless and energy-constrained distributed system.Due to the dynamic change of communication environment and the instability of wireless link,Ad Hoc Network is facing great challenges in service availability.In this thesis,a fault-based quantitative evaluation model of traffic availability in Ad Hoc networks is proposed.By studying the multi-state Markov fault model of communication networks and the performance analysis of delay index based on CSMA\CA protocol,the quantitative evaluation of traffic availability in Ad Hoc networks is realized.And the availability of tactical unit network under different typical network configuration conditions is analyzed through experiments.

FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers

The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service.Failure prediction is an important means of ensuring service availability.Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics,and the distribution imbalance between the failure sample and the normal sample is widespread,resulting in inaccurate failure prediction.Targeting these challenges,this paper proposes a novel failure prediction method FP-STE(Failure Prediction based on Spatio-temporal Feature Extraction).Firstly,an improved recurrent neural network HW-GRU(Improved GRU based on HighWay network)and a convolutional neural network CNN are used to extract the temporal features and spatial features of multivariate data respectively to increase the discrimination of different types of failure symptoms which improves the accuracy of prediction.Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the future.SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning.Experimental results based on real data sets confirm the effectiveness and superiority of FP-STE.

System Architecture of Godson-3 Multi-Core Processors

Godson-3 is the latest generation of Godson microprocessor family. It takes a scalable multi-core architecture with hardware support for accelerating applications including X86 emulation and signal processing. This paper introduces the system architecture of Godson-3 from various aspects including system scalability, organization of memory hierarchy, network-on-chip, inter-chip connection and I/O subsystem.