Mobile cloud computing （MCC） has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile dev...Mobile cloud computing （MCC） has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods （such as Monte- Carlo simulation） may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are def＇med to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.展开更多
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 evalua...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.展开更多
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...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.展开更多
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 pa...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.展开更多
基金Project supported by the National Natural Science Foundation of China （Nos. 61402127 and 61370212） and the Natural Science Foundation of Heilongjiang Province, China （No. F2015029）
文摘Mobile cloud computing （MCC） has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods （such as Monte- Carlo simulation） may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are def＇med to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.
基金We would like to acknowledge support from the Foundation of School of Computer Science and Engineering,Xi’an Technological University(gsysi2016012)the Equipment Advance Research Project(41402020202).
文摘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.
基金supported in part by National Key Research and Development Program of China(2019YFB2103200)NSFC(61672108),Open Subject Funds of Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory(SKX182010049)+1 种基金the Fundamental Research Funds for the Central Universities(5004193192019PTB-019)the Industrial Internet Innovation and Development Project 2018 of China.
文摘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.
基金Supported by the National High Technology Development 863 Program of China under Grant No.2008AA010901the National Natural Science Foundation of China under Grant Nos.60736012 and 60673146the National Basic Research 973 Program of China under Grant No.2005CB321601.
文摘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.
