2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery最新文献_第9页

A Trust Management Model in Centralized Cognitive Radio Networks 集中式认知无线网络中的信任管理模型

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.104

Qingqi Pei, Rui Liang, Hongning Li

引用次数: 18

A Simplified Solution of a PEPA Model of Kerberos Protocol Kerberos协议PEPA模型的简化解

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.50

Yishi Zhao, N. Thomas

引用次数: 1

A Noise-Robust EASI Algorithm for Noisy Blind Interference-Signal Separation 噪声盲干扰信号分离的EASI算法

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.92

Yu-ling Duan, Hang Zhang

引用次数: 3

A Framwork for a University Display Platform by Using Cloud Computing 基于云计算的高校展示平台框架

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.75

Zhen-heng Liu, Changxin Zhang, T. Reynolds

引用次数: 1

An Approach to Improving Trust Estimation in Service-Based Systems 一种改进基于服务系统信任估计的方法

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.64

S. Yau, Peng Sun

引用次数: 1

QoS Evaluation of HTTP over Satellites 基于卫星的HTTP服务质量评价

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.38

L. Audah, Zhili Sun, H. Cruickshank

引用次数: 2

A QoS Approach for Detecting and Managing a Fault Alarm Storm 基于QoS的故障告警风暴检测与管理方法

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.30

Jian Yang, Li Li, Su-bin Shen, C. Yang

{"title":"A QoS Approach for Detecting and Managing a Fault Alarm Storm","authors":"Jian Yang, Li Li, Su-bin Shen, C. Yang","doi":"10.1109/CyberC.2011.30","DOIUrl":"https://doi.org/10.1109/CyberC.2011.30","url":null,"abstract":"When a large communication network encounters abnormal situations, such as multiple cable cuts or a failure of interface protocol leading to large numbers of propagating alarms may potentially cause many symptom events in multiple network domains. As a result, a network management system may be overwhelmed with a heavy alarm load scenario (a.k.a alarm storm). Appropriate fault management tools are required to provide auto-monitoring capabilities which indicate the health and status of the system and auto-alerting capabilities, which alert to conditions that impact system performance. This paper provides a method and strategy for managing a fault alarm storm by detecting unusual system performance behavior and implementing a reliably handling mechanism to maintain adequate system application performance. The paper explores a QoS approach to calculate inter-process latency based on dynamic states of various types of alarms and behaviors of system performance. It provides quality of service trending projection functionality of inter-process latency to manage CPU and memory capacity and to trigger the control mechanism in order to protect system resources. Based on this approach, it enables more efficient/accurate system performance measurement algorithms to handle the most import alarms need to be taken care of at the most critical situations.","PeriodicalId":227472,"journal":{"name":"2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127233446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Method Based on Kalman Filtering Theory for Solving Two Point Boundary Value Problems 基于卡尔曼滤波理论求解两点边值问题的新方法

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.57

Zhaohui Dang, Yulin Zhang

引用次数: 0

Using UICDS to Share Data in the Real-Time Decision Support System for Pandemic Response 在大流行应对的实时决策支持系统中使用UICDS共享数据

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.106

Robert Kelley, Aman Gupta, Anup Kumar, S. Heragu

引用次数: 4

Non-Bayesian Learning of Channel Sensing Order for Dynamic Spectrum Access Networks 动态频谱接入网络中信道感知顺序的非贝叶斯学习

2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery Pub Date : 2011-10-10 DOI: 10.1109/CyberC.2011.91

Bowen Li, Panlong Yang, Jinlong Wang, Qi-hui Wu, Nan Xia

{"title":"Non-Bayesian Learning of Channel Sensing Order for Dynamic Spectrum Access Networks","authors":"Bowen Li, Panlong Yang, Jinlong Wang, Qi-hui Wu, Nan Xia","doi":"10.1109/CyberC.2011.91","DOIUrl":"https://doi.org/10.1109/CyberC.2011.91","url":null,"abstract":"In this paper, we consider sequential channel sensing and accessing with unknown channel availability probabilities. In such sequential channel sensing framework, it is critical important to arrange the channel sensing order properly at the beginning of each time slot, so as to balance the tradeoff between finding an idle channel as soon as possible in current slot (exploitation by sensing currently looks better channel preferentially) and making the channel statistics more precise to benefit the following sensing order arrangement (exploration by sensing the channels currently looks suboptimal). To handle this tradeoff balancing problem, we propose five online learning algorithms: Pure Exploitation (PE), Exploitation with Optimism Initial Estimation (EOIE), UCB-based Order (UCBO), $varepsilon$-greedy Order and SoftMax Order. The latter three algorithms are extended from three classic algorithms on handling multi-armed bandit problem: UCB1cite{Auer_02}, $varepsilon$-greedycite{Cesa_98} and SoftMaxcite{Cesa_98}. Among these algorithms, UCBO is the only one that we have proved to be a zero-regret strategy, which means the system is guaranteed to achieve optimal sensing order by applying UCBO, if enough slots are played. We then evaluate the algorithms by simulations. The results are a little surprising but meaningful. We find that the PE algorithm, which is treated as a simple strategy with poor performance for traditional multi-armed bandit problems, performs very well in the order learning problem when the number of channels is small. And our proposed EOIE algorithm performs nearly optimal in all cases. In contrast, the UCB-based Order converges too slowly for actual application, although it provides theoretical zero-regret guarantee as we have proved.","PeriodicalId":227472,"journal":{"name":"2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114964910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2