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Predicting Query Duplication with Box-Jenkins Models and Its Applications

Predicting Query Duplication with Box-Jenkins Models and Its...

引用

International Conference on Peer-to-Peer Computing

作者： Xinyao Hu Shicong Meng Cong Shi Dingyi Han Yong Yu APEX Data and Knowledge Management Laboratory Shanghai Jiaotong University Shanghai China

Many previous works of Peer-to-Peer traffic characterization and modeling focused their attention on the distribution of query contents. However, few has been done towards a better understanding of the time series distribution of these queries, which is vital for system performance. To remedy this situation, this paper characterizes query traffic by using automatic time series analysis to evaluate different linear models(Box-Jenkins models and some simple windowed-mean models) for predicting the number of duplicated queries from 10 minutes to 2 hours into the future. Both the predictive power and the computational costs of these models are evaluated over 318,942,450 real world Gnutella queries collected over 3 months. We find the number of duplicated queries is consistently predictable. Simple, practical models like AR perform well on prediction. To show that these characteristics have a wide range of potential applications, we propose two enhancement to existing search results caching and load balancing algorithms. Our simulation study shows that our methodology works quite well in both scenarios in terms of efficiency and effectiveness. The main contribution of this paper lies in: (1) proposing new measurement techniques on Gnutella, (2) characterizing and modeling peer-to-peer query traffic with Box-Jenkins Models, (3) presenting a general enhancement to existing performance optimization algorithm in P2P systems.

关键词： Predictive models Traffic control Power system modeling Peer to peer computing System performance Time series analysis Computational efficiency Load management Measurement techniques Optimization

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Cuckoo Ring: BalancingWorkload for Locality Sensitive Hash

Cuckoo Ring: BalancingWorkload for Locality Sensitive Hash

引用

International Conference on Peer-to-Peer Computing

作者： Dingyi Han Ting Shen Shicong Meng Yong Yu Apex Data & Knowledge Management Laboratory Department of Computer Science & Engineering Shanghai Jiaotong University Shanghai China

Locality sensitive hash (LSH) is widely used in peer-to-peer (P2P) systems. Although it can support range or similarity queries, it breaks the load balance mechanism of traditional distributed hash table (DHT) based system by replacing consistent hash with LSH. To solve the imbalance problem, current systems either weaken the locality preserve ability from similarity preserved to order preserved or adopt load aware peer join mechanism. The first method does not support similarity query as it loses the similarity information and the second method is greatly affected by the dynamic nature of P2P networks. In this paper, we propose a novel system, cuckoo ring, which can preserve similarity information while load balanced. It does not guide the newly joining peer to the hot areas but move the items in the hot areas to cold areas so that the short life time peers are distributed uniformly across the network instead of being guided to the hot areas. Compared to traditional DHT systems, cuckoo ring only maintains a little more information about the global light load peers and the moved indexed items

关键词： Indexing Load management knowledge management Computer science data engineering knowledge engineering Nearest neighbor searches Q measurement

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A unified model of pollution in P2P networks

A unified model of pollution in P2P networks

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Cong Shi Dingyi Han Xinyao Hu Yong Yu Apex Data and Knowledge Management Laboratory Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

Nowadays many popular Peer-to-Peer (P2P) systems suffer from the simultaneous attacks of various pollution, including file-targeted attack and index-targeted attack. However, to our knowledge, there is no model that takes both of them into consideration. In fact, the two attacks impact the effect of each other. It makes the models considering either kind of pollution only fail to accurately illustrate the actual pollution. In this paper, we develop a unified model to remedy the defect. Through the analysis from the perspective of user behavior, the two attacks are integrated into the unified model as two factors impacting users' choice of the files to download. The modeled file proliferation processes are consistent to those measured in real P2P systems. Moreover, the co-effect of the two attacks is also analyzed. The extremum point of co-effect is found, which corresponds to the most efficient attack of pollution. Further analysis of the model's accuracy requires the quantitative comparison between the modeled effects of pollution and the measured ones. Nonetheless, no such metric has ever been proposed, which also causes a lot of problems in evaluating the effect of pollution and anti-pollution techniques. To fix the deficiency, we propose several metrics to assess the effect of pollution, including abort ratio, average download time of unpolluted files, etc. These metrics estimate the effect of pollution from different aspects. They are applied to the analysis of pollution emulated by our unified model. The co-effect of pollution is captured by these metrics. Furthermore, the difference between our model and the previously developed ones is also reflected by them.

关键词： Pollution measurement Peer to peer computing Internet Marketing and sales Mathematical model knowledge management Computer science data engineering knowledge engineering Traffic control

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Reinforcement Learning for Query-Oriented Routing Indices in Unstructured Peer-to-Peer Networks

Reinforcement Learning for Query-Oriented Routing Indices in...

引用

International Conference on Peer-to-Peer Computing

作者： Cong Shi Shicong Meng Yuanjie Liu Dingyi Han Yong Yu Apex Data and Knowledge Management Laboratory Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

The idea of building query-oriented routing indices has changed the way of improving routing efficiency from the basis as it can learn the content distribution during the query routing process. It gradually improves routing efficiency with no excessive network overhead of the routing index construction and maintenance. However, the previously proposed mechanism is not practically effective due to the slow improvement of routing efficiency. In this paper, we propose a novel mechanism for query-oriented routing indices which quickly achieves high routing efficiency at low cost. The maintenance method employs reinforcement learning to utilize mass peer behaviors to construct and maintain routing indices. It explicitly uses the expected value of returned content number to depict the content distribution, which helps quickly approximate the real distribution. Meanwhile, the routing method is to retrieve as many contents as possible. It also helps speed up the learning process further. The experimental evaluation shows that the mechanism has high routing efficiency, quick learning ability and satisfactory performance under churn

关键词： Learning Routing Peer to peer computing Query processing Buildings knowledge management Computer science data engineering knowledge engineering Costs

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Mining and Predicting Duplication over Peer-to-Peer Query Streams

Mining and Predicting Duplication over Peer-to-Peer Query St...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Shicong Meng Yifeng Shao Cong Shi Dingyi Han Yong Yu APEX Data and Knowledge Management Laboratory Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

Many previous works of data mining user queries in peer-to-peer systems focused their attention on the distribution of query contents. However, few has been done towards a better understanding of the time series distribution of these queries, which is vital for system performance. To remedy this situation, this paper mines query steams by using automatic time series analysis to evaluate different linear models (Box-Jenkins models and some simple windowed-mean models) for predicting the number of duplicated queries from 10 minutes to 2 hours into the future. Both the predictive power and the computational costs of these models are evaluated over 318,942,450 real world Gnutella queries collected over 3 months. We find the number of duplicated queries is consistently predictable. Simple, practical models like AR perform well on prediction

关键词： Peer to peer computing Predictive models Power system modeling data mining System performance Crawlers Time series analysis Computational efficiency Load management knowledge management

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