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Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale IP Backbone Networks

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Traffic Measurement Optimization Based on Reinforcement Lear...

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Huizhi Wang Laisen Nie Zhaolong Ning Mohammad S. Obaidat Runze Shang School of Electronics and Information Northwestern Polytechnical University Xi'an P.R. China Qingdao Research Institute of Northwestern Polytechnical University Qingdao P.R. China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

The end-to-end network traffic information is the basis of network management in large-scale IP backbone networks. To obtain exact network traffic data, a prevalent idea is to employ NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, it also affects the network load. Motivated by this issue, we propose an optimized traffic measurement method based on reinforcement learning in this paper, which can collect most of the network traffic data by activating NetFlow on a subset of interfaces of routers in a network. We use the Q- learning-based approach to deal with the problem of the interface-selection, and propose an approach to compute the reward. Furthermore, a modified Q- learning approach is proposed to handle the problem of interface-selection. The method is evaluated by the real data from the Abilene and GEANT backbone networks. Simulation results show that the proposed method can improve the efficiency of traffic measurement distinctly.

关键词： Learning (artificial intelligence) Optimization Routing IP networks Tomography Monitoring Estimation

Fundamentals of an E-Government Interoperability Policy: Case Study of Uganda

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SSRN

SSRN 2018年

作者： Ngabirano, Silas Kanagwa, Benjamin Ministry of ICT Department of Information Management Services Kampala Uganda Makerere University School of Computing and Informatics Technology Kampala Uganda

Globally, more government products and services are being delivered through the use of information systems. This has led to great improvements in the quality of public service delivery. However, for the case of Uganda, full realization of the benefits of computerization of government services is constrained by the inability of some of the e-government systems to interoperate. This is because most of these systems have continued to be built in silos with inadequate attention to the need to share data and processes. This has resulted into the existence of many heterogeneous systems that have limited interfacing and whose development is unsynchronized. © 2018, The Authors. All rights reserved.

关键词： Information use

Learning clinical outcomes from heterogeneous genomic data sources

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arXiv 2019年

作者： Yousefi, Safoora Shaban, Amirreza Amgad, Mohamed Chandradevan, Ramraj Cooper, Lee A.D. Department of Computer Science Emory University AtlantaGA United States College of Computing Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States Department of Electronic and Telecommunication Engineering University of Moratuwa Moratuwa Sri Lanka

Translating the vast data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine largely due to small number of independent samples. In this paper, we show that neural networks can be trained to predict clinical outcomes using heterogeneous genomic data sources via multi-task learning and adversarial representation learning, allowing one to combine multiple cohorts and outcomes in training. We compare our proposed method to two baselines and demonstrate that it can be used to help mitigate the data scarcity and clinical outcome censorship in cancer genomics learning problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Automated Reconfigurable Antenna Impedance for Optimum Power Transfer

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Automated Reconfigurable Antenna Impedance for Optimum Power...

Asia-Pacific Conference on Microwave

作者： Mohammad Alibakhshikenari Bal S. Virdee Chan H. See Raed A. Abd-Alhameed Francisco Falcone Ernesto Limiti University of Rome “Tor Vergata” Via del Politecnico 1 Rome ITALY London Metropolitan University Center for Communications Technology School of Computing & Digital Media London UK School of Engineering University of Bolton Deane Road Bolton UK Faculty of Engineering and Informatics University of Bradford Bradford West Yorkshire UK Universidad Pública de Navarra SPAIN

ISBN: (数字)9781728135175

ISBN: (纸本)9781728135182

This paper presents an approach to implement an automatically tuning antenna for optimising power transfer suitable for software defined radio (SDR). Automatic tuning is accomplished using a closed loop impedance tuning network comprising of an impedance sensor and control unit. The sensor provides the control unit with data on the transmit or receive power, and the algorithm is used to impedance of a T-network of LC components to optimize the antenna impedance to maximise power transmission or reception. The effectiveness of the proposed tuning algorithm in relation to impedance matching and convergence on the optimum matching network goal is shown to be superior compared with the conventional tuning algorithm.

关键词： Impedance Tuning Transmitting antennas Impedance matching Receiving antennas Wireless communication

Global optimum encoding packet selection mechanism based on opportunistic network coding for wireless network retransmission

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The Journal of China Universities of Posts and Telecommunications 2017年第1期24卷 47-59页

作者： Wang Lian Peng Daiyuan Liang Hongbin School of Information Science and Technology Southwest Jiaotong University Chengdu 610031 China School of Computing Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Transportation and Logistics Southwest Jiaotong University Chengdu 610031 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85281-8809 USA

Packet loss cannot be avoided in wireless network due to wireless transmission medium particularity, therefore improving retransmission efficiency is meaningful to wireless transmission. The current retransmission packet selection mechanisms based on oppornistic network coding （ONC） face low retransmission efficiency and high computational complexity problems. To these problems, an optimized encoding packet selection mechanism based on ONC in wireless network retransmission （OONCR） is proposed. This mechanism is based on mutual exclusion packets and decoding gain concepts, and makes full use of ONC advantages. The main contributions of this scheme are to control the algorithm eomplexity of the maximum encoding packets selection effectively, avoid the redundancy encoding packets due to the overlapping among encoding packets, and take the encoding packet local and global optimization problem into consideration. Retransmission efficiency is evaluated according to the computational complexity, the throughput, the retransmission redundancy ratio, and the number of average retransmission. Under the various conditions, the number of average retransmission of OONCR is mainly lower than that of other typical retransmission packet selection schemes. The average retransmission redundancy ratios of OONCR are lower about 5%-40% compared with other typical schemes. Simultaneously the computational complexity of OONCR is comparatively lower than that of other typical schemes.

关键词： wireless network retransmission network coding throughput

Taxi Demand Prediction with LSTM-Based Combination Model

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Taxi Demand Prediction with LSTM-Based Combination Model

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yongxuan Lai Kaixin Zhang Junqiang Lin Fan Yang Yi Fan Shenzhen Research Institute Xiamen University Shenzhen China School of Informatics Xiamen University Xiamen China Department of Automation Xiamen University Xiamen China School of Mathematics and Statistics Key Laboratory of Complex Systems and Intelligent Computing Qiannan Normal University for Nationalities Duyun China Guangxi Key Lab of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Taxi demands to predict in urban city helps to guide the drivers to cruise and pickup riders, which increases drivers' income and decreases riders' waiting time. The taxi demands prediction problem is usually cast as a time series prediction task, and deep models such as recurrent neural network (RNN) or long short-term memory (LSTM) is adopted for the prediction. In this paper, we present a LSTM-based combination model to predict taxi demand based on the recent demand and other relevant information. More specifically, we use a spatio-temporal component to capture the spatio-temporal information and use an attribute component to obtain external information (e.g. weather, point of interest), and these two components are combined to make the final predictions. We evaluate our method of two real-world datasets, and the results show that the proposed approach outperforms other prediction methods.

关键词： Public transportation Urban areas Predictive models Time series analysis Meteorology Autoregressive processes Data models

Access balancing in storage systems by labeling partial steiner systems

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arXiv 2019年

作者： Chee, Yeow Meng Colbourn, Charles J. Dau, Hoang Gabrys, Ryan Ling, Alan C.H. Lusi, Dylan Milenkovic, Olgica Department of Industrial Systems Engineering and Management National University of Singapore Singapore School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Computer Science and Information Technology RMIT University Melbourne Australia Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States Department of Computer Science University of Vermont BurlingtonVT United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL United States

Storage archtectures ranging from minimum bandwidth regenerating encoded distributed storage systems to declustered-parity RAIDs can be designed using dense partial Steiner systems in order to support fast reads, writes, and recovery of failed storage units. In order to ensure good performance, popularities of the data items should be taken into account and the frequencies of accesses to the storage units made as uniform as possible. A proposed combinatorial model ranks items by popularity and assigns data items to elements in a dense partial Steiner system so that the sums of ranks of the elements in each block are as equal as possible. By developing necessary conditions in terms of independent sets, we demonstrate that certain Steiner systems must have a much larger difference between the largest and smallest block sums than is dictated by an elementary lower bound. In contrast, we also show that certain dense partial S(t,t+1,v) designs can be labeled to realize the elementary lower bound. Furthermore, we prove that for every admissible order v, there is a Steiner triple system (S(2,3,v)) whose largest difference in block sums is within an additive constant of the lower bound. Copyright © 2019, The Authors. All rights reserved.

关键词： Multiprocessing systems

Extracting human emotions at different places based on facial expressions and spatial clustering analysis

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arXiv 2019年

作者： Kang, Yuhao Jia, Qingyuan Gao, Song Zeng, Xiaohuan Wang, Yueyao Angsuesser, Stephan Liu, Yu Ye, Xinyue Fei, Teng Geospatial Data Science Lab Department of Geography University of Wisconsin Madison United States School of Resources and Environmental Sciences Wuhan University Wuhan China College of Urban and Environment Sciences Peking University Beijing China Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China Urban Informatics and Spatial Computing Lab Department of Informatics New Jersey Institute of Technology Newark United States

The emergence of big data enables us to evaluate the various human emotions at places from a statistic perspective by applying affective computing. In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user generated footprints collected in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state-of-the-art computer vision techniques. And two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected out from over 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

Oscillatory evolution of collective behavior in evolutionary games played with reinforcement learning

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arXiv 2019年

作者： Zhang, Si-Ping Zhang, Ji-Qiang Chen, Li Liu, Xu-Dong Key Laboratory of Biomedical Information Engineering Ministry of Education Key Laboratory of Neuro-informatics & Rehabilitation Engineering Ministry of Civil Affairs Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an710049 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Physics and Information Technology Shaanxi Normal University Xi'an710062 China

Large-scale cooperation underpins the evolution of ecosystems and the human society, and the collective behaviors by self-organization of multi-agent systems are the key for understanding. As artificial intelligence (AI) prevails in almost all branches of science, it would be of great interest to see what new insights of collective behavior could be obtained from a multi-agent AI system. Here, we introduce a typical reinforcement learning (RL) algorithm - Q learning into evolutionary game dynamics, where agents pursue optimal action on the basis of the introspectiveness rather than the birth-death or imitation processes in the traditional evolutionary game (EG). We investigate the cooperation prevalence numerically for a general 2 × 2 game setting. We find that the cooperation prevalence in the multi-agent AI is amazingly of equal level as in the traditional EG in most cases. However, in the snowdrift games with RL we also reveal that explosive cooperation appears in the form of periodic oscillation, and we study the impact of the payoff structure on its emergence. Finally, we show that the periodic oscillation can also be observed in some other EGs with the RL algorithm, such as the rock-paper-scissors game. Our results offer a reference point to understand emergence of cooperation and oscillatory behaviors in nature and society from AI's perspective. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning